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Continual Face Forgery Detection via Historical Distribution Preserving (2308.06217v1)
Published 11 Aug 2023 in cs.CV
Abstract: Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall short of practical application. Additionally, finetuning these methods on historical training data is resource-intensive in terms of time and storage. In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones. Specifically, we propose a Historical Distribution Preserving (HDP) framework that reserves and preserves the distributions of historical faces. To achieve this, we use universal adversarial perturbation (UAP) to simulate historical forgery distribution, and knowledge distillation to maintain the distribution variation of real faces across different models. We also construct a new benchmark for CFFD with three evaluation protocols. Our extensive experiments on the benchmarks show that our method outperforms the state-of-the-art competitors.
- Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: Faceforensics++: Learning to detect manipulated facial images. In: ICCV, pp. 1–11 (2019) Dolhansky et al. [2020] Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., Ferrer, C.C.: The deepfake detection challenge dataset. arXiv preprint arXiv:2006.07397 (2020) Chen et al. [2021] Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., Ji, R.: Local relation learning for face forgery detection. AAAI (2021) Qian et al. [2020] Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: ECCV, pp. 86–103 (2020). Springer Dang et al. [2020] Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., Ferrer, C.C.: The deepfake detection challenge dataset. arXiv preprint arXiv:2006.07397 (2020) Chen et al. [2021] Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., Ji, R.: Local relation learning for face forgery detection. AAAI (2021) Qian et al. [2020] Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: ECCV, pp. 86–103 (2020). Springer Dang et al. [2020] Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., Ji, R.: Local relation learning for face forgery detection. AAAI (2021) Qian et al. [2020] Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: ECCV, pp. 86–103 (2020). Springer Dang et al. [2020] Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: ECCV, pp. 86–103 (2020). Springer Dang et al. [2020] Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. 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[2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. 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[2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., Ji, R.: Local relation learning for face forgery detection. AAAI (2021) Qian et al. [2020] Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: ECCV, pp. 86–103 (2020). Springer Dang et al. [2020] Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: ECCV, pp. 86–103 (2020). Springer Dang et al. [2020] Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. 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IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. 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[2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. 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Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. 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Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. 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Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. 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[2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: Face forgery detection by mining frequency-aware clues. In: ECCV, pp. 86–103 (2020). Springer Dang et al. [2020] Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). 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[2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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[2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. 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[2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. 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[2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: CVPR, pp. 5781–5790 (2020) Afchar et al. [2018] Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: WIFS, pp. 1–7 (2018). IEEE Sun et al. [2021] Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. 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[2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Ye, Q., Liu, J., Gao, Y., Shao, L., Ji, R.: Domain general face forgery detection by learning to weight. In: AAAI, vol. 35, pp. 2638–2646 (2021) Luo et al. [2023] Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. 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[2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. 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[2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. 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NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). 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Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. 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Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. 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[2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., Kot, A.C.: Beyond the prior forgery knowledge: Mining critical clues for general face forgery detection. arXiv preprint arXiv:2304.12489 (2023) Luo et al. [2021] Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, A., Li, E., Liu, Y., Kang, X., Wang, Z.J.: A capsule network based approach for detection of audio spoofing attacks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6359–6363 (2021). IEEE Li et al. [2020] Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. 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[2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. 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Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. 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[2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., Guo, B.: Face x-ray for more general face forgery detection. In: CVPR, pp. 5001–5010 (2020) Sun et al. [2022] Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Yao, T., Chen, S., Ding, S., Ji, R., et al.: Dual contrastive learning for general face forgery detection. In: AAAI (2022) Luo et al. [2021] Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. 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[2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: CVPR, pp. 16317–16326 (2021) Li et al. [2023] Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. 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In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. 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Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. 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Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. 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IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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[2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. 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In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. 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[2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Li, C., Huang, Z., Paudel, D.P., Wang, Y., Shahbazi, M., Hong, X., Van Gool, L.: A continual deepfake detection benchmark: Dataset, methods, and essentials. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339–1349 (2023) Li and Hoiem [2017] Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Z., Hoiem, D.: Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40(12), 2935–2947 (2017) Hou et al. [2019] Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: CVPR, pp. 831–839 (2019) Kirkpatrick et al. [2017] Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. 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[2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences 114(13), 3521–3526 (2017) Rusu et al. [2016] Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016) Xu and Zhu [2018] Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. 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Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. 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Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. 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[2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. 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[2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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[2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. 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[2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Xu, J., Zhu, Z.: Reinforced continual learning. NeurIPS 31 (2018) Verma et al. [2021] Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. 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Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. 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[2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Verma, V.K., Liang, K.J., Mehta, N., Rai, P., Carin, L.: Efficient feature transformations for discriminative and generative continual learning. In: CVPR, pp. 13865–13875 (2021) Rebuffi et al. [2017] Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. 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[2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. 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[2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. 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[2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. 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[2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Rebuffi, S.-A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: CVPR, pp. 2001–2010 (2017) Aljundi et al. [2019] Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Online continual learning with no task boundaries. arXiv preprint arXiv:1903.08671 (2019) Chaudhry et al. [2020] Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaudhry, A., Gordo, A., Dokania, P.K., Torr, P., Lopez-Paz, D.: Using hindsight to anchor past knowledge in continual learning. arXiv preprint arXiv:2002.08165 3 (2020) Jetley et al. [2018] Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. 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[2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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[2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Jetley, S., Lord, N., Torr, P.: With friends like these, who needs adversaries? NeurIPS 31 (2018) Zhang et al. [2020] Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhang, C., Benz, P., Imtiaz, T., Kweon, I.S.: Understanding adversarial examples from the mutual influence of images and perturbations. In: CVPR, pp. 14521–14530 (2020) Matern et al. [2019] Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. 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Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. 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[2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. 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Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. 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Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. 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In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Matern, F., Riess, C., Stamminger, M.: Exploiting visual artifacts to expose deepfakes and face manipulations. In: WACVW, pp. 83–92 (2019). IEEE Yang et al. [2019] Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP, pp. 8261–8265 (2019). IEEE Stehouwer et al. [2019] Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Stehouwer, J., Dang, H., Liu, F., Liu, X., Jain, A.: On the detection of digital face manipulation. arXiv preprint arXiv:1910.01717 (2019) Zhao et al. [2021a] Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. 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NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. 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In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. CVPR (2021) Zhao et al. [2021b] Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., Xia, W.: Learning self-consistency for deepfake detection. In: CVPR, pp. 15023–15033 (2021) Delange et al. [2021] Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Aljundi et al. [2018] Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T.: Memory aware synapses: Learning what (not) to forget. In: ECCV, pp. 139–154 (2018) Wang et al. [2021] Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. 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[2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., Pfister, T.: Learning to prompt for continual learning. arXiv preprint arXiv:2112.08654 (2021) Buzzega et al. [2020] Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. 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[2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark experience for general continual learning: a strong, simple baseline. NeurIPS 33, 15920–15930 (2020) Wu et al. [2019] Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Fu, Y.: Large scale incremental learning. In: CVPR, pp. 374–382 (2019) Moosavi-Dezfooli et al. [2017] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR, pp. 1765–1773 (2017) Szegedy et al. [2013] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013) Moosavi-Dezfooli et al. [2016] Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. 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Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. 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In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. 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[2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
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[2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Moosavi-Dezfooli, S.-M., Fawzi, A., Frossard, P.: Deepfool: a simple and accurate method to fool deep neural networks. In: CVPR, pp. 2574–2582 (2016) Moosavi-Dezfooli et al. [2018] Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Soatto, S.: Robustness of classifiers to universal perturbations: A geometric perspective. ICLR (2018) Chaubey et al. [2020] Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Chaubey, A., Agrawal, N., Barnwal, K., Guliani, K.K., Mehta, P.: Universal adversarial perturbations: A survey. arXiv preprint arXiv:2005.08087 (2020) Li et al. [2020] Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: A large-scale challenging dataset for deepfake forensics. In: CVPR, pp. 3207–3216 (2020) Zi et al. [2020] Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Zi, B., Chang, M., Chen, J., Ma, X., Jiang, Y.-G.: Wilddeepfake: A challenging real-world dataset for deepfake detection. In: ACM MM, pp. 2382–2390 (2020) Tan and Le [2019] Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Tan, M., Le, Q.V.: Efficientnet: Rethinking model scaling for convolutional neural networks. ICML (2019) Deng et al. [2009] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009). Ieee Li et al. [2019] Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: CVPR, pp. 5060–5069 (2019) Mai et al. [2021] Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Mai, Z., Li, R., Kim, H., Sanner, S.: Supervised contrastive replay: Revisiting the nearest class mean classifier in online class-incremental continual learning. In: CVPR, pp. 3589–3599 (2021) Kim et al. [2021] Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Kim, M., Tariq, S., Woo, S.S.: Fretal: Generalizing deepfake detection using knowledge distillation and representation learning. In: CVPR, pp. 1001–1012 (2021) Yan et al. [2021] Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Yan, S., Xie, J., He, X.: Der: Dynamically expandable representation for class incremental learning. In: CVPR, pp. 3014–3023 (2021) Cha et al. [2021] Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: ICCV, pp. 9516–9525 (2021) Sun et al. [2022] Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., Ji, R.: An information theoretic approach for attention-driven face forgery detection. In: ECCV, pp. 111–127 (2022). Springer Zhuang et al. [2022] Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Zhuang, W., Chu, Q., Tan, Z., Liu, Q., Yuan, H., Miao, C., Luo, Z., Yu, N.: Uia-vit: Unsupervised inconsistency-aware method based on vision transformer for face forgery detection. In: European Conference on Computer Vision, pp. 391–407 (2022). Springer
- Ke Sun (136 papers)
- Shen Chen (29 papers)
- Taiping Yao (40 papers)
- Xiaoshuai Sun (91 papers)
- Shouhong Ding (90 papers)
- Rongrong Ji (315 papers)