Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Dealing with Subject Similarity in Differential Morphing Attack Detection (2404.07667v1)

Published 11 Apr 2024 in cs.CV and cs.CR

Abstract: The advent of morphing attacks has posed significant security concerns for automated Face Recognition systems, raising the pressing need for robust and effective Morphing Attack Detection (MAD) methods able to effectively address this issue. In this paper, we focus on Differential MAD (D-MAD), where a trusted live capture, usually representing the criminal, is compared with the document image to classify it as morphed or bona fide. We show these approaches based on identity features are effective when the morphed image and the live one are sufficiently diverse; unfortunately, the effectiveness is significantly reduced when the same approaches are applied to look-alike subjects or in all those cases when the similarity between the two compared images is high (e.g. comparison between the morphed image and the accomplice). Therefore, in this paper, we propose ACIdA, a modular D-MAD system, consisting of a module for the attempt type classification, and two modules for the identity and artifacts analysis on input images. Successfully addressing this task would allow broadening the D-MAD applications including, for instance, the document enroLLMent stage, which currently relies entirely on human evaluation, thus limiting the possibility of releasing ID documents with manipulated images, as well as the automated gates to detect both accomplices and criminals. An extensive cross-dataset experimental evaluation conducted on the introduced scenario shows that ACIdA achieves state-of-the-art results, outperforming literature competitors, while maintaining good performance in traditional D-MAD benchmarks.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (52)
  1. The magic passport. In IEEE International Joint Conference on Biometrics (IJCB’14), pages 1–7, 2014.
  2. Biometric systems under morphing attacks: Assessment of morphing techniques and vulnerability reporting. In 2017 International Conference of the Biometrics Special Interest Group (BIOSIG), pages 1–7. IEEE, 2017.
  3. Morphing attack detection-database, evaluation platform, and benchmarking. IEEE transactions on information forensics and security, 16:4336–4351, 2020.
  4. Incremental training of face morphing detectors. In 2022 26th International Conference on Pattern Recognition (ICPR), pages 914–921. IEEE, 2022.
  5. Deep face representations for differential morphing attack detection. IEEE transactions on information forensics and security, 15:3625–3639, 2020.
  6. Towards minimizing efforts for morphing attacks–deep embeddings for morphing pair selection and improved morphing attack detection. arXiv preprint arXiv:2305.18216, 2023.
  7. A double siamese framework for differential morphing attack detection. Sensors, 21(10):3466, 2021.
  8. Decoupling texture blending and shape warping in face morphing. In 2019 international conference of the biometrics special interest group (BIOSIG), pages 1–5. IEEE, 2019.
  9. Combining identity features and artifact analysis for differential morphing attack detection. In International Conference on Image Analysis and Processing, pages 100–111. Springer, 2023.
  10. Face demorphing. IEEE Transactions on Information Forensics and Security, 13(4):1008–1017, 2017.
  11. Biolab. FVC-onGoing. https://biolab.csr.unibo.it/fvcongoing/. Accessed: 2022-11-30.
  12. National Institute of Standards and Technology. NIST FRVT Morph.
  13. Fingerprint and on-line signature verification competitions at icb 2009. In Advances in Biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings 3, pages 725–732. Springer, 2009.
  14. Magface: A universal representation for face recognition and quality assessment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14225–14234, 2021.
  15. Bsif: Binarized statistical image features. In Proceedings of the 21st international conference on pattern recognition (ICPR2012), pages 1363–1366. IEEE, 2012.
  16. Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In Proceedings of 12th international conference on pattern recognition, volume 1, pages 582–585. IEEE, 1994.
  17. Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), volume 1, pages 886–893. Ieee, 2005.
  18. Towards detection of morphed face images in electronic travel documents. In 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pages 187–192. IEEE, 2018.
  19. Detecting morphed face images using facial landmarks. In Image and Signal Processing: 8th International Conference, ICISP 2018, Cherbourg, France, July 2-4, 2018, Proceedings 8, pages 444–452. Springer, 2018.
  20. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  21. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690–4699, 2019.
  22. Fd-gan: Face de-morphing generative adversarial network for restoring accomplice’s facial image. IEEE Access, 7:75122–75131, 2019.
  23. Differential morph face detection using discriminative wavelet sub-bands. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1425–1434, 2021.
  24. Fused classification for differential face morphing detection. arXiv preprint arXiv:2309.00665, 2023.
  25. Fusion of face demorphing and deep face representations for differential morphing attack detection. In 2022 International Conference of the Biometrics Special Interest Group (BIOSIG), pages 1–5. IEEE, 2022.
  26. Face morphing attack detection and localization based on feature-wise supervision. IEEE Transactions on Information Forensics and Security, 17:3649–3662, 2022.
  27. Reliable face morphing attack detection in on-the-fly border control scenario with variation in image resolution and capture distance. In 2022 IEEE International Joint Conference on Biometrics (IJCB), pages 1–10. IEEE, 2022.
  28. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE signal processing letters, 23(10):1499–1503, 2016.
  29. Improved residual networks for image and video recognition. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 9415–9422. IEEE, 2021.
  30. Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14, pages 87–102. Springer, 2016.
  31. Vggface2: A dataset for recognising faces across pose and age. In 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pages 67–74. IEEE, 2018.
  32. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9, 2015.
  33. The ar face database: Cvc technical report, 24. 1998.
  34. Overview of the face recognition grand challenge. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), volume 1, pages 947–954. IEEE, 2005.
  35. The feret database and evaluation procedure for face-recognition algorithms. Image and vision computing, 16(5):295–306, 1998.
  36. Are gan-based morphs threatening face recognition? In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2959–2963. IEEE, 2022.
  37. Satya Mallick. “Face morph using opencv — c++ / python. https://learnopencv.com/face-morph-using-opencv-cpp-python/. Accessed: 2022-11-30.
  38. Alyssa Quek. FaceMorpher morphing algorithm. https://github.com/alyssaq/face_morpher. Accessed: 2022-11-30.
  39. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110–8119, 2020.
  40. Face research lab london set. Psychol. Methodol. Des. Anal, 2017.
  41. A new ranking method for principal components analysis and its application to face image analysis. Image and vision computing, 28(6):902–913, 2010.
  42. FaceFusion. Facefusion. http://www.wearemoment.com/FaceFusion/. Accessed: 2022-11-30.
  43. Morph creation and vulnerability of face recognition systems to morphing. In Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks, pages 117–137. Springer International Publishing Cham, 2022.
  44. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  45. Davis E King. Dlib-ml: A machine learning toolkit. The Journal of Machine Learning Research, 10:1755–1758, 2009.
  46. Sface: Sigmoid-constrained hypersphere loss for robust face recognition. IEEE Transactions on Image Processing, 30:2587–2598, 2021.
  47. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815–823, 2015.
  48. Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on faces in’Real-Life’Images: detection, alignment, and recognition, 2008.
  49. On the vulnerability and detection of digital morphed and scanned face images. In Proc. Int. Workshop on Biometrics and Forensics (IWBF), 2017.
  50. Detecting face morphing attacks by analyzing the directed distances of facial landmarks shifts. In Pattern Recognition: 40th German Conference, GCPR 2018, Stuttgart, Germany, October 9-12, 2018, Proceedings 40, pages 518–534. Springer, 2019.
  51. Robust morph-detection at automated border control gate using deep decomposed 3d shape & diffuse reflectance. In 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pages 106–112. IEEE, 2019.
  52. Modeling attacks on photo-id documents and applying media forensics for the detection of facial morphing. In Proceedings of the 5th ACM workshop on information hiding and multimedia security, pages 21–32, 2017.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now