Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
60 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
8 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Security and Privacy on Generative Data in AIGC: A Survey (2309.09435v3)

Published 18 Sep 2023 in cs.CR

Abstract: The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media forgery for fraudulent purposes. Consequently, both academia and industry begin to emphasize the trustworthiness of generative data, successively providing a series of countermeasures for security and privacy. In this survey, we systematically review the security and privacy on generative data in AIGC, particularly for the first time analyzing them from the perspective of information security properties. Specifically, we reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance, respectively. Finally, we show some representative benchmarks, present a statistical analysis, and summarize the potential exploration directions from each of theses properties.

PDF HTML Abstract

Security and Privacy on Generative Data in AIGC: A Comprehensive Examination

The paper "Security and Privacy on Generative Data in AIGC: A Survey" by Wang et al. provides a detailed exploration of the security and privacy issues within the domain of Artificial Intelligence-Generated Content (AIGC). As the capabilities of generative models like GANs and DMs reach new heights, the paper underscores the necessity of scrutiny in handling generative data, especially considering privacy leakages, media forgeries, and other security vulnerabilities.

Overview and Classification of Issues

The paper systematically categorizes issues associated with generative data under four core properties of information security: privacy, controllability, authenticity, and compliance.

  1. Privacy: The paper explores two perspectives:
    • Privacy in AIGC: Where generative data potentially mirrors sensitive content from its training data.
    • AIGC for Privacy: Capitalizing on generative data to replace or obscure sensitive information in real data.
  2. Controllability: It stresses the importance of controlling access to generative data, highlighting:
    • Access Control: Techniques like adversarial perturbations prevent unauthorized manipulations.
    • Traceability: Methods such as digital watermarking and blockchain for tracking the origin and use of generative data.
  3. Authenticity: The challenge is distinguishing between real and generative data, with emphasis on:
    • Generative Detection: Identifying generative data using its inherent artifacts or traces.
    • Generative Attribution: Tracing generative outputs back to their respective models.
  4. Compliance: It examines the regulatory landscape ensuring generative data adheres to standards of:
    • Non-toxicity: Preventing the generation of harmful content.
    • Factuality: Ensuring that the generated content is accurate and not misleading.

Numerical Results and Countermeasures

The paper provides a substantive foundation by presenting diverse methods and empirical results for tackling these identified threats. For example, differential privacy methods are shown to offer respectable utility while ensuring data protection, though challenges remain in maintaining fidelity across tasks. Techniques such as Stable Signature and DiffusionShield underscore the advancement in watermarking methods, improving traceability despite potential robustness challenges. Additionally, the emergent drive towards provable privacy through differential privacy and adversarial perturbations is particularly noteworthy, albeit complex in balancing with utility.

Implications and Future Directions in AIGC

The research highlights the pressing need for foundational advancements to effectively bridge the gap between theoretical frameworks and practical implementations. As models scale, the balancing act between mitigating privacy risks and maintaining data utility becomes increasingly critical. Future efforts should emphasize enhancing the robustness and scalability of adversarial perturbation and watermarking techniques across diverse generative architectures. Moreover, addressing the nuanced challenges posed by compliance, particularly in terms of bias and fairness, remains an urgent area for further exploration.

The survey by Wang et al. provides a thorough expertise-based roadmap for understanding and addressing the cybersecurity and privacy nuances in AIGC. As AI technologies continue to evolve and permeate our digital infrastructure, such comprehensive analyses are indispensable in guiding both academic inquiry and industry practices towards more secure and privacy-preserving generative systems.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (135)
  1. Y. Wang, Z. Su, N. Zhang, R. Xing, D. Liu, T. H. Luan, and X. Shen, “A survey on metaverse: Fundamentals, security, and privacy,” IEEE Communications Surveys & Tutorials, 2022.
  2. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020.
  3. T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4401–4410.
  4. A. Clark, J. Donahue, and K. Simonyan, “Efficient video generation on complex datasets,” arXiv preprint arXiv:1907.06571, vol. 2, no. 3, p. 4, 2019.
  5. J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems, vol. 33, pp. 6840–6851, 2020.
  6. K. Yang, A. M. Swope, A. Gu, R. Chalamala, P. Song, S. Yu, S. Godil, R. Prenger, and A. Anandkumar, “Leandojo: Theorem proving with retrieval-augmented language models,” arXiv preprint arXiv:2306.15626, 2023.
  7. “Gartner identifies the top strategic technology trends for 2022,” https://www.gartner.com/en/newsroom/press-releases/2021-10-18-gartner-identifies-the-top-strategic-technology, 2022.
  8. N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson et al., “Extracting training data from large language models,” in 30th USENIX Security Symposium (USENIX Security 21), 2021, pp. 2633–2650.
  9. N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramer, B. Balle, D. Ippolito, and E. Wallace, “Extracting training data from diffusion models,” arXiv preprint arXiv:2301.13188, 2023.
  10. “Fact check: Was there an explosion at the pentagon?” https://www.newsweek.com, 2023.
  11. P. Korshunov and S. Marcel, “Deepfakes: a new threat to face recognition? assessment and detection,” arXiv preprint arXiv:1812.08685, 2018.
  12. “Interim regulation on the management of generative artificial intelligence (ai) services,” https://www.gov.cn/zhengce/zhengceku/202307/content_6891752.htm, 2023.
  13. Y. Wang, Y. Pan, M. Yan, Z. Su, and T. H. Luan, “A survey on chatgpt: Ai-generated contents, challenges, and solutions,” arXiv preprint arXiv:2305.18339, 2023.
  14. C. Chen, J. Fu, and L. Lyu, “A pathway towards responsible ai generated content,” arXiv preprint arXiv:2303.01325, 2023.
  15. C. Chen, Z. Wu, Y. Lai, W. Ou, T. Liao, and Z. Zheng, “Challenges and remedies to privacy and security in aigc: Exploring the potential of privacy computing, blockchain, and beyond,” arXiv preprint arXiv:2306.00419, 2023.
  16. Y. Hu, W. Kuang, Z. Qin, K. Li, J. Zhang, Y. Gao, W. Li, and K. Li, “Artificial intelligence security: Threats and countermeasures,” ACM Comput. Surv., vol. 55, no. 1, nov 2021.
  17. B. Liu, M. Ding, S. Shaham, W. Rahayu, F. Farokhi, and Z. Lin, “When machine learning meets privacy: A survey and outlook,” ACM Comput. Surv., vol. 54, no. 2, mar 2021.
  18. J.-W. Chen, L.-J. Chen, C.-M. Yu, and C.-S. Lu, “Perceptual indistinguishability-net (pi-net): Facial image obfuscation with manipulable semantics,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 6478–6487.
  19. R. Plant, V. Giuffrida, and D. Gkatzia, “You are what you write: Preserving privacy in the era of large language models,” arXiv preprint arXiv:2204.09391, 2022.
  20. C. Meehan, K. Chaudhuri, and S. Dasgupta, “A non-parametric test to detect data-copying in generative models,” in International Conference on Artificial Intelligence and Statistics, 2020.
  21. K. Tirumala, A. Markosyan, L. Zettlemoyer, and A. Aghajanyan, “Memorization without overfitting: Analyzing the training dynamics of large language models,” Advances in Neural Information Processing Systems, vol. 35, pp. 38 274–38 290, 2022.
  22. N. Carlini, D. Ippolito, M. Jagielski, K. Lee, F. Tramer, and C. Zhang, “Quantifying memorization across neural language models,” in International Conference on Learning Representations (ICLR), 2023.
  23. R. Webster, J. Rabin, L. Simon, and F. Jurie, “Detecting overfitting of deep generative networks via latent recovery,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11 273–11 282.
  24. Q. Feng, C. Guo, F. Benitez-Quiroz, and A. M. Martinez, “When do gans replicate? on the choice of dataset size,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 6701–6710.
  25. R. Webster, “A reproducible extraction of training images from diffusion models,” arXiv preprint arXiv:2305.08694, 2023.
  26. G. Somepalli, V. Singla, M. Goldblum, J. Geiping, and T. Goldstein, “Diffusion art or digital forgery? investigating data replication in diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 6048–6058.
  27. A. Bai, C.-J. Hsieh, W. Kan, and H.-T. Lin, “Reducing training sample memorization in gans by training with memorization rejection,” arXiv preprint arXiv:2210.12231, 2022.
  28. “Dall·e 2 pre-training mitigations,” https://openai.com/research/dall-e-2-pre-training-mitigations, 2022.
  29. N. Kandpal, E. Wallace, and C. Raffel, “Deduplicating training data mitigates privacy risks in language models,” in International Conference on Machine Learning.   PMLR, 2022, pp. 10 697–10 707.
  30. M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, 2016, pp. 308–318.
  31. C. Ma, J. Li, M. Ding, B. Liu, K. Wei, J. Weng, and H. V. Poor, “Rdp-gan: A rényi-differential privacy based generative adversarial network,” IEEE Transactions on Dependable and Secure Computing, 2023.
  32. T. Dockhorn, T. Cao, A. Vahdat, and K. Kreis, “Differentially private diffusion models,” arXiv preprint arXiv:2210.09929, 2022.
  33. S. Ghalebikesabi, L. Berrada, S. Gowal, I. Ktena, R. Stanforth, J. Hayes, S. De, S. L. Smith, O. Wiles, and B. Balle, “Differentially private diffusion models generate useful synthetic images,” arXiv preprint arXiv:2302.13861, 2023.
  34. “Clip retrieval system,” https://rom1504:github:io/clip-retrieval/, 2022.
  35. T. Shaik, X. Tao, H. Xie, L. Li, X. Zhu, and Q. Li, “Exploring the landscape of machine unlearning: A survey and taxonomy,” arXiv preprint arXiv:2305.06360, 2023.
  36. N. Kumari, B. Zhang, S.-Y. Wang, E. Shechtman, R. Zhang, and J.-Y. Zhu, “Ablating concepts in text-to-image diffusion models,” in Proceedings of the IEEE International Conference on Computer Vision, 2023.
  37. E. Zhang, K. Wang, X. Xu, Z. Wang, and H. Shi, “Forget-me-not: Learning to forget in text-to-image diffusion models,” arXiv preprint arXiv:2303.17591, 2023.
  38. T. Wang, Y. Zhang, R. Zhao, W. Wen, and R. Lan, “Identifiable face privacy protection via virtual identity transformation,” IEEE Signal Processing Letters, 2023.
  39. H. Hukkelås, R. Mester, and F. Lindseth, “Deepprivacy: A generative adversarial network for face anonymization,” in International symposium on visual computing.   Springer, 2019, pp. 565–578.
  40. M. Gong, J. Liu, H. Li, Y. Xie, and Z. Tang, “Disentangled representation learning for multiple attributes preserving face deidentification,” IEEE transactions on neural networks and learning systems, vol. 33, no. 1, pp. 244–256, 2020.
  41. Z. Yuan, Z. You, S. Li, Z. Qian, X. Zhang, and A. Kot, “On generating identifiable virtual faces,” in Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 1465–1473.
  42. M. Kim, F. Liu, A. Jain, and X. Liu, “Dcface: Synthetic face generation with dual condition diffusion model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 12 715–12 725.
  43. J. Liu, C. P. Lau, and R. Chellappa, “Diffprotect: Generate adversarial examples with diffusion models for facial privacy protection,” arXiv preprint arXiv:2305.13625, 2023.
  44. Y. Lyu, Y. Jiang, Z. He, B. Peng, Y. Liu, and J. Dong, “3d-aware adversarial makeup generation for facial privacy protection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 13 438–13 453, 2023.
  45. T. Wang, Y. Zhang, Z. Yang, H. Zhang, and Z. Hua, “Seeing is not believing: An identity hider for human vision privacy protection,” arXiv preprint arXiv:2307.00481, 2023.
  46. C. Cao and M. Li, “Generating mobility trajectories with retained data utility,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 2610–2620.
  47. F. Liu, Z. Cheng, H. Chen, Y. Wei, L. Nie, and M. Kankanhalli, “Privacy-preserving synthetic data generation for recommendation systems,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022, pp. 1379–1389.
  48. V. Thambawita, P. Salehi, S. A. Sheshkal, S. A. Hicks, H. L. Hammer, S. Parasa, T. d. Lange, P. Halvorsen, and M. A. Riegler, “Singan-seg: Synthetic training data generation for medical image segmentation,” PloS one, vol. 17, no. 5, p. e0267976, 2022.
  49. Z. Yao, Q. Liu, J. Yang, Y. Chen, and Z. Wu, “Ppup-gan: A gan-based privacy-protecting method for aerial photography,” Future Generation Computer Systems, vol. 145, pp. 284–292, 2023.
  50. Y. S. Hindistan and E. F. Yetkin, “A hybrid approach with gan and dp for privacy preservation of iiot data,” IEEE Access, vol. 11, pp. 5837–5849, 2023.
  51. Y. Lu, H. Wang, and W. Wei, “Machine learning for synthetic data generation: a review,” arXiv preprint arXiv:2302.04062, 2023.
  52. C.-Y. Yeh, H.-W. Chen, H.-H. Shuai, D.-N. Yang, and M.-S. Chen, “Attack as the best defense: Nullifying image-to-image translation gans via limit-aware adversarial attack,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 16 188–16 197.
  53. Z. Li, N. Yu, A. Salem, M. Backes, M. Fritz, and Y. Zhang, “UnGANable: Defending Against GAN-based Face Manipulation,” in USENIX Security Symposium (USENIX Security).   USENIX, 2023.
  54. Y. Zhu, Y. Chen, X. Li, R. Zhang, X. Tian, B. Zheng, and Y. Chen, “Information-containing adversarial perturbation for combating facial manipulation systems,” IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2046–2059, 2023.
  55. T. Van Le, H. Phung, T. H. Nguyen, Q. Dao, N. Tran, and A. Tran, “Anti-dreambooth: Protecting users from personalized text-to-image synthesis,” arXiv preprint arXiv:2303.15433, 2023.
  56. S. Shan, J. Cryan, E. Wenger, H. Zheng, R. Hanocka, and B. Y. Zhao, “Glaze: Protecting artists from style mimicry by text-to-image models,” arXiv preprint arXiv:2302.04222, 2023.
  57. R. Wu, Y. Wang, H. Shi, Z. Yu, Y. Wu, and D. Liang, “Towards prompt-robust face privacy protection via adversarial decoupling augmentation framework,” 2023.
  58. C. Liang, X. Wu, Y. Hua, J. Zhang, Y. Xue, T. Song, Z. Xue, R. Ma, and H. Guan, “Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples,” in International Conference on Machine Learning.   PMLR, 2023, pp. 20 763–20 786.
  59. N. Yu, V. Skripniuk, S. Abdelnabi, and M. Fritz, “Artificial fingerprinting for generative models: Rooting deepfake attribution in training data,” in Proceedings of the IEEE/CVF International conference on computer vision, 2021, pp. 14 448–14 457.
  60. Y. Zhao, T. Pang, C. Du, X. Yang, N.-M. Cheung, and M. Lin, “A recipe for watermarking diffusion models,” arXiv preprint arXiv:2303.10137, 2023.
  61. P. Fernandez, G. Couairon, H. Jégou, M. Douze, and T. Furon, “The stable signature: Rooting watermarks in latent diffusion models,” arXiv preprint arXiv:2303.15435, 2023.
  62. S. Peng, Y. Chen, C. Wang, and X. Jia, “Protecting the intellectual property of diffusion models by the watermark diffusion process,” arXiv preprint arXiv:2306.03436, 2023.
  63. C. Xiong, C. Qin, G. Feng, and X. Zhang, “Flexible and secure watermarking for latent diffusion model,” in Proceedings of the 31st ACM International Conference on Multimedia, ser. MM ’23.   New York, NY, USA: Association for Computing Machinery, 2023, p. 1668–1676. [Online]. Available: https://doi.org/10.1145/3581783.3612448
  64. D. S. Ong, C. S. Chan, K. W. Ng, L. Fan, and Q. Yang, “Protecting intellectual property of generative adversarial networks from ambiguity attacks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 3630–3639.
  65. Y. Liu, Z. Li, M. Backes, Y. Shen, and Y. Zhang, “Watermarking diffusion model,” arXiv preprint arXiv:2305.12502, 2023.
  66. H. Yao, J. Lou, K. Ren, and Z. Qin, “Promptcare: Prompt copyright protection by watermark injection and verification,” in IEEE Symposium on Security and Privacy (S&P).   IEEE, 2024.
  67. Y. Zeng, M. Zhou, Y. Xue, and V. M. Patel, “Securing deep generative models with universal adversarial signature,” arXiv preprint arXiv:2305.16310, 2023.
  68. Y. Ma, Z. Zhao, X. He, Z. Li, M. Backes, and Y. Zhang, “Generative watermarking against unauthorized subject-driven image synthesis,” arXiv preprint arXiv:2306.07754, 2023.
  69. Y. Cui, J. Ren, H. Xu, P. He, H. Liu, L. Sun, and J. Tang, “Diffusionshield: A watermark for copyright protection against generative diffusion models,” arXiv preprint arXiv:2306.04642, 2023.
  70. W. Feng, J. He, J. Zhang, T. Zhang, W. Zhou, W. Zhang, and N. Yu, “Catch you everything everywhere: Guarding textual inversion via concept watermarking,” arXiv preprint arXiv:2309.05940, 2023.
  71. C. Liu, J. Zhang, T. Zhang, X. Yang, W. Zhang, and N. Yu, “Detecting voice cloning attacks via timbre watermarking,” in Network and Distributed System Security Symposium, 2024.
  72. Y. Liu, H. Du, D. Niyato, J. Kang, Z. Xiong, C. Miao, A. Jamalipour et al., “Blockchain-empowered lifecycle management for ai-generated content (aigc) products in edge networks,” arXiv preprint arXiv:2303.02836, 2023.
  73. Z. Jiang, J. Zhang, and N. Z. Gong, “Evading watermark based detection of ai-generated content,” arXiv preprint arXiv:2305.03807, 2023.
  74. Z. Lu, D. Huang, L. Bai, X. Liu, J. Qu, and W. Ouyang, “Seeing is not always believing: A quantitative study on human perception of ai-generated images,” arXiv preprint arXiv:2304.13023, 2023.
  75. Y. Mirsky and W. Lee, “The creation and detection of deepfakes: A survey,” ACM Computing Surveys (CSUR), vol. 54, no. 1, pp. 1–41, 2021.
  76. N.-M. Aliman and L. Kester, “Vr, deepfakes and epistemic security,” in 2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), 2022, pp. 93–98.
  77. L. Verdoliva, “Media forensics and deepfakes: an overview,” IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 5, pp. 910–932, 2020.
  78. S. Hussain, P. Neekhara, M. Jere, F. Koushanfar, and J. McAuley, “Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples,” in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 3347–3356.
  79. A. Pegoraro, K. Kumari, H. Fereidooni, and A.-R. Sadeghi, “To chatgpt, or not to chatgpt: That is the question!” arXiv preprint arXiv:2304.01487, 2023.
  80. S. Jia, M. Huang, Z. Zhou, Y. Ju, J. Cai, and S. Lyu, “Autosplice: A text-prompt manipulated image dataset for media forensics,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 893–903.
  81. J. J. Bird and A. Lotfi, “Cifake: Image classification and explainable identification of ai-generated synthetic images,” arXiv preprint arXiv:2303.14126, 2023.
  82. M. Zhu, H. Chen, Q. Yan, X. Huang, G. Lin, W. Li, Z. Tu, H. Hu, J. Hu, and Y. Wang, “Genimage: A million-scale benchmark for detecting ai-generated image,” arXiv preprint arXiv:2306.08571, 2023.
  83. R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, and L. Verdoliva, “On the detection of synthetic images generated by diffusion models,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).   IEEE, 2023, pp. 1–5.
  84. Z. Xi, W. Huang, K. Wei, W. Luo, and P. Zheng, “Ai-generated image detection using a cross-attention enhanced dual-stream network,” arXiv preprint arXiv:2306.07005, 2023.
  85. S. Sinitsa and O. Fried, “Deep image fingerprint: Accurate and low budget synthetic image detector,” arXiv preprint arXiv:2303.10762, 2023.
  86. S.-Y. Wang, A. A. Efros, J.-Y. Zhu, and R. Zhang, “Evaluating data attribution for text-to-image models,” in ICCV, 2023.
  87. Z. Wang, J. Bao, W. Zhou, W. Wang, H. Hu, H. Chen, and H. Li, “Dire for diffusion-generated image detection,” arXiv preprint arXiv:2303.09295, 2023.
  88. R. Amoroso, D. Morelli, M. Cornia, L. Baraldi, A. Del Bimbo, and R. Cucchiara, “Parents and children: Distinguishing multimodal deepfakes from natural images,” arXiv preprint arXiv:2304.00500, 2023.
  89. N. Zhong, Y. Xu, Z. Qian, and X. Zhang, “Rich and poor texture contrast: A simple yet effective approach for ai-generated image detection,” arXiv preprint arXiv:2311.12397, 2023.
  90. P. Dogoulis, G. Kordopatis-Zilos, I. Kompatsiaris, and S. Papadopoulos, “Improving synthetically generated image detection in cross-concept settings,” in Proceedings of the 2nd ACM International Workshop on Multimedia AI against Disinformation, 2023, pp. 28–35.
  91. X. Bi, B. Liu, F. Yang, B. Xiao, W. Li, G. Huang, and P. C. Cosman, “Detecting generated images by real images only,” arXiv preprint arXiv:2311.00962, 2023.
  92. S. Gehrmann, H. Strobelt, and A. M. Rush, “Gltr: Statistical detection and visualization of generated text,” arXiv preprint arXiv:1906.04043, 2019.
  93. E. Mitchell, Y. Lee, A. Khazatsky, C. D. Manning, and C. Finn, “Detectgpt: Zero-shot machine-generated text detection using probability curvature,” arXiv preprint arXiv:2301.11305, 2023.
  94. E. Tulchinskii, K. Kuznetsov, L. Kushnareva, D. Cherniavskii, S. Barannikov, I. Piontkovskaya, S. Nikolenko, and E. Burnaev, “Intrinsic dimension estimation for robust detection of ai-generated texts,” arXiv preprint arXiv:2306.04723, 2023.
  95. B. Guo, X. Zhang, Z. Wang, M. Jiang, J. Nie, Y. Ding, J. Yue, and Y. Wu, “How close is chatgpt to human experts? comparison corpus, evaluation, and detection,” arXiv preprint arXiv:2301.07597, 2023.
  96. Y. Chen, H. Kang, V. Zhai, L. Li, R. Singh, and B. Ramakrishnan, “Gpt-sentinel: Distinguishing human and chatgpt generated content,” arXiv preprint arXiv:2305.07969, 2023.
  97. X. He, X. Shen, Z. Chen, M. Backes, and Y. Zhang, “Mgtbench: Benchmarking machine-generated text detection,” arXiv preprint arXiv:2303.14822, 2023.
  98. T. Bui, N. Yu, and J. Collomosse, “Repmix: Representation mixing for robust attribution of synthesized images,” in European Conference on Computer Vision.   Springer, 2022, pp. 146–163.
  99. T. Yang, D. Wang, F. Tang, X. Zhao, J. Cao, and S. Tang, “Progressive open space expansion for open-set model attribution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 15 856–15 865.
  100. Z. Sha, Z. Li, N. Yu, and Y. Zhang, “De-fake: Detection and attribution of fake images generated by text-to-image diffusion models,” arXiv preprint arXiv:2210.06998, 2022.
  101. P. Lorenz, R. Durall, and J. Keuper, “Detecting images generated by deep diffusion models using their local intrinsic dimensionality,” arXiv preprint arXiv:2307.02347, 2023.
  102. L. Guarnera, O. Giudice, and S. Battiato, “Level up the deepfake detection: a method to effectively discriminate images generated by gan architectures and diffusion models,” arXiv preprint arXiv:2303.00608, 2023.
  103. N. Yu, L. S. Davis, and M. Fritz, “Attributing fake images to gans: Learning and analyzing gan fingerprints,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 7556–7566.
  104. T. Yang, Z. Huang, J. Cao, L. Li, and X. Li, “Deepfake network architecture attribution,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 4, 2022, pp. 4662–4670.
  105. S. Girish, S. Suri, S. S. Rambhatla, and A. Shrivastava, “Towards discovery and attribution of open-world gan generated images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 14 094–14 103.
  106. “Blueprint for an ai bill of rights,” https://www.whitehouse.gov/ostp/ai-bill-of-rights/, 2022.
  107. “The artificial intelligence act,” https://artificialintelligenceact.eu/, 2023.
  108. Y. Qu, X. Shen, X. He, M. Backes, S. Zannettou, and Y. Zhang, “Unsafe diffusion: On the generation of unsafe images and hateful memes from text-to-image models,” arXiv preprint arXiv:2305.13873, 2023.
  109. A. Birhane, V. U. Prabhu, and E. Kahembwe, “Multimodal datasets: misogyny, pornography, and malignant stereotypes,” arXiv preprint arXiv:2110.01963, 2021.
  110. A. Caliskan, J. J. Bryson, and A. Narayanan, “Semantics derived automatically from language corpora contain human-like biases,” Science, vol. 356, no. 6334, pp. 183–186, 2017.
  111. E. Sheng, K.-W. Chang, P. Natarajan, and N. Peng, “The woman worked as a babysitter: On biases in language generation,” arXiv preprint arXiv:1909.01326, 2019.
  112. A. Abid, M. Farooqi, and J. Zou, “Persistent anti-muslim bias in large language models,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021, pp. 298–306.
  113. “Stable diffusion github repository,” https://github.com/CompVis/stable-diffusion, 2022.
  114. “Dall-e 2 creates incredible images—and biased ones you don’t see,” https://www.wired.com/story/dall-e-2-ai-text-image-bias-social-media/, 2022.
  115. “Disinformation researchers raise alarms about a.i. chatbots,” https://www.nytimes.com/2023/02/08/technology/ai-chatbots-disinformation.html, 2023.
  116. E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, “On the dangers of stochastic parrots: Can language models be too big?” in Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, 2021, pp. 610–623.
  117. P. Henderson, M. Krass, L. Zheng, N. Guha, C. D. Manning, D. Jurafsky, and D. Ho, “Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset,” Advances in Neural Information Processing Systems, vol. 35, pp. 29 217–29 234, 2022.
  118. D. Ganguli, L. Lovitt, J. Kernion, A. Askell, Y. Bai, S. Kadavath, B. Mann, E. Perez, N. Schiefer, K. Ndousse et al., “Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned,” arXiv preprint arXiv:2209.07858, 2022.
  119. M. Brack, F. Friedrich, P. Schramowski, and K. Kersting, “Mitigating inappropriateness in image generation: Can there be value in reflecting the world’s ugliness?” arXiv preprint arXiv:2305.18398, 2023.
  120. P. Schramowski, M. Brack, B. Deiseroth, and K. Kersting, “Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 22 522–22 531.
  121. R. Gandikota, J. Materzynska, J. Fiotto-Kaufman, and D. Bau, “Erasing concepts from diffusion models,” arXiv preprint arXiv:2303.07345, 2023.
  122. A. Heng and H. Soh, “Selective amnesia: A continual learning approach to forgetting in deep generative models,” arXiv preprint arXiv:2305.10120, 2023.
  123. J. Rando, D. Paleka, D. Lindner, L. Heim, and F. Tramèr, “Red-teaming the stable diffusion safety filter,” arXiv preprint arXiv:2210.04610, 2022.
  124. O. Evans, O. Cotton-Barratt, L. Finnveden, A. Bales, A. Balwit, P. Wills, L. Righetti, and W. Saunders, “Truthful ai: Developing and governing ai that does not lie,” arXiv preprint arXiv:2110.06674, 2021.
  125. B. Goodrich, V. Rao, P. J. Liu, and M. Saleh, “Assessing the factual accuracy of generated text,” in proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019, pp. 166–175.
  126. N. Lee, W. Ping, P. Xu, M. Patwary, P. N. Fung, M. Shoeybi, and B. Catanzaro, “Factuality enhanced language models for open-ended text generation,” Advances in Neural Information Processing Systems, vol. 35, pp. 34 586–34 599, 2022.
  127. A. Alaa, B. Van Breugel, E. S. Saveliev, and M. van der Schaar, “How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models,” in International Conference on Machine Learning.   PMLR, 2022, pp. 290–306.
  128. A. Azaria and T. Mitchell, “The internal state of an llm knows when its lying,” arXiv preprint arXiv:2304.13734, 2023.
  129. Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch, “Improving factuality and reasoning in language models through multiagent debate,” arXiv preprint arXiv:2305.14325, 2023.
  130. Z. Gou, Z. Shao, Y. Gong, Y. Shen, Y. Yang, N. Duan, and W. Chen, “Critic: Large language models can self-correct with tool-interactive critiquing,” arXiv preprint arXiv:2305.11738, 2023.
  131. C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman et al., “Laion-5b: An open large-scale dataset for training next generation image-text models,” Advances in Neural Information Processing Systems, vol. 35, pp. 25 278–25 294, 2022.
  132. A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” arXiv preprint arXiv:2112.10741, 2021.
  133. “stable-diffusion-safety-checker,” https://huggingface.co/CompVis/stable-diffusion-safety-checker, 2022.
  134. T. Markov, C. Zhang, S. Agarwal, F. E. Nekoul, T. Lee, S. Adler, A. Jiang, and L. Weng, “A holistic approach to undesired content detection in the real world,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 12, 2023, pp. 15 009–15 018.
  135. J. Lu, H. Lin, X. Zhang, Z. Li, T. Zhang, L. Zong, F. Ma, and B. Xu, “Hate speech detection via dual contrastive learning,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Tao Wang (700 papers)
  2. Yushu Zhang (43 papers)
  3. Shuren Qi (10 papers)
  4. Ruoyu Zhao (12 papers)
  5. Zhihua Xia (21 papers)
  6. Jian Weng (50 papers)
Citations (29)
View on Semantic Scholar
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube