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Privacy-Preserving Face Recognition Using Trainable Feature Subtraction (2403.12457v1)

Published 19 Mar 2024 in cs.CV

Abstract: The widespread adoption of face recognition has led to increasing privacy concerns, as unauthorized access to face images can expose sensitive personal information. This paper explores face image protection against viewing and recovery attacks. Inspired by image compression, we propose creating a visually uninformative face image through feature subtraction between an original face and its model-produced regeneration. Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation. To enhance privacy, the high-dimensional representation is crafted through random channel shuffling, resulting in randomized recognizable images devoid of attacker-leverageable texture details. We distill our methodologies into a novel privacy-preserving face recognition method, MinusFace. Experiments demonstrate its high recognition accuracy and effective privacy protection. Its code is available at https://github.com/Tencent/TFace.

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References (68)
  1. Simple functional encryption schemes for inner products. In Public-Key Cryptography - PKC 2015 - 18th IACR International Conference on Practice and Theory in Public-Key Cryptography, Gaithersburg, MD, USA, March 30 - April 1, 2015, Proceedings, pages 733–751. Springer, 2015.
  2. Sface: Privacy-friendly and accurate face recognition using synthetic data. In 2022 IEEE International Joint Conference on Biometrics (IJCB), pages 1–11. IEEE, 2022.
  3. Idiff-face: Synthetic-based face recognition through fizzy identity-conditioned diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 19650–19661, 2023.
  4. Privacy preserving face recognition utilizing differential privacy. Comput. Secur., 97:101951, 2020.
  5. Discriminant-component eigenfaces for privacy-preserving face recognition. In 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016, Vietri sul Mare, Salerno, Italy, September 13-16, 2016, pages 1–6. IEEE, 2016.
  6. Geometry-aware guided loss for deep crack recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4703–4712, 2022.
  7. Transface: Calibrating transformer training for face recognition from a data-centric perspective. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 20642–20653, 2023.
  8. Arcface: Additive angular margin loss for deep face recognition. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pages 4690–4699. Computer Vision Foundation / IEEE, 2019.
  9. Inverting visual representations with convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4829–4837, 2016.
  10. Ovgu Ozturk Ergun. Privacy preserving face recognition in encrypted domain. In 2014 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2014, Ishigaki, Japan, November 17-20, 2014, pages 643–646. IEEE, 2014.
  11. Privacy-preserving face recognition. In Privacy Enhancing Technologies, 9th International Symposium, PETS 2009, Seattle, WA, USA, August 5-7, 2009. Proceedings, pages 235–253. Springer, 2009.
  12. Privacy-preserving face recognition with multi-edge assistance for intelligent security systems. IEEE Internet of Things Journal, 2023.
  13. 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, pages 87–102. Springer, 2016.
  14. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 770–778. IEEE Computer Society, 2016.
  15. Model inversion attacks against collaborative inference. In Proceedings of the 35th Annual Computer Security Applications Conference, pages 148–162, 2019.
  16. A study on fuzzy clustering-based k-anonymization for privacy preserving crowd movement analysis with face recognition. In 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015, Fukuoka, Japan, November 13-15, 2015, pages 37–41. IEEE, 2015.
  17. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst, 2007.
  18. Efficient privacy-preserving face identification protocol. IEEE Transactions on Services Computing, 2023.
  19. Improving adversarial robustness of masked autoencoders via test-time frequency-domain prompting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1600–1610, 2023a.
  20. Instahide: Instance-hiding schemes for private distributed learning. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, pages 4507–4518. PMLR, 2020a.
  21. Curricularface: adaptive curriculum learning loss for deep face recognition. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5901–5910, 2020b.
  22. Collaborative diffusion for multi-modal face generation and editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6080–6090, 2023b.
  23. Grote: Group testing for privacy-preserving face identification. In Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy, pages 117–128, 2023.
  24. Privacy-preserving face recognition with learnable privacy budgets in frequency domain. In Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XII, pages 475–491. Springer, 2022.
  25. Face recognition with renewable and privacy preserving binary templates. In Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID 2005), 16-18 October 2005, Buffalo, NY, USA, pages 21–26. IEEE Computer Society, 2005.
  26. Efficient and privacy-preserving distributed face recognition scheme via facenet. In ACM TURC 2021: ACM Turing Award Celebration Conference - Hefei, China, 30 July 2021 - 1 August 2021, pages 110–115. ACM, 2021.
  27. Privacy-preserving lightweight face recognition. Neurocomputing, 363:212–222, 2019.
  28. Sphereface: Deep hypersphere embedding for face recognition. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 6738–6746. IEEE Computer Society, 2017.
  29. Lightweight privacy-preserving ensemble classification for face recognition. IEEE Internet Things J., 6(3):5778–5790, 2019.
  30. On the reconstruction of face images from deep face templates. IEEE transactions on pattern analysis and machine intelligence, 41(5):1188–1202, 2018.
  31. A privacy-preserving deep learning approach for face recognition with edge computing. In USENIX Workshop on Hot Topics in Edge Computing, HotEdge 2018, Boston, MA, July 10, 2018. USENIX Association, 2018.
  32. IARPA janus benchmark - C: face dataset and protocol. In 2018 International Conference on Biometrics, ICB 2018, Gold Coast, Australia, February 20-23, 2018, pages 158–165. IEEE, 2018.
  33. Privacy-enhancing face biometrics: A comprehensive survey. IEEE Trans. Inf. Forensics Secur., 16:4147–4183, 2021.
  34. An overview of privacy-enhancing technologies in biometric recognition. CoRR, abs/2206.10465, 2022.
  35. Duetface: Collaborative privacy-preserving face recognition via channel splitting in the frequency domain. In MM ’22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10 - 14, 2022, pages 6755–6764. ACM, 2022.
  36. Privacy-preserving face recognition using random frequency components. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 19673–19684, 2023.
  37. Not all features are equal: Discovering essential features for preserving prediction privacy. In WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, pages 669–680. ACM / IW3C2, 2021.
  38. Semi-adversarial networks: Convolutional autoencoders for imparting privacy to face images. In 2018 International Conference on Biometrics, ICB 2018, Gold Coast, Australia, February 20-23, 2018, pages 82–89. IEEE, 2018a.
  39. Gender privacy: An ensemble of semi adversarial networks for confounding arbitrary gender classifiers. In 9th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2018, Redondo Beach, CA, USA, October 22-25, 2018, pages 1–10. IEEE, 2018b.
  40. Agedb: The first manually collected, in-the-wild age database. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2017, Honolulu, HI, USA, July 21-26, 2017, pages 1997–2005. IEEE Computer Society, 2017.
  41. A privacy-preserving biometric recognition system with visual cryptography. Advances in Multimedia, 2022, 2022.
  42. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference Munich, Germany, October 5 - 9, 2015, Proceedings, Part III, pages 234–241. Springer, 2015.
  43. Efficient privacy-preserving face recognition. In Information, Security and Cryptology - ICISC 2009, 12th International Conference, Seoul, Korea, December 2-4, 2009, Revised Selected Papers, pages 229–244. Springer, 2009.
  44. Frontal to profile face verification in the wild. In 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016, Lake Placid, NY, USA, March 7-10, 2016, pages 1–9. IEEE Computer Society, 2016.
  45. Freeu: Free lunch in diffusion u-net. arXiv preprint arXiv:2309.11497, 2023.
  46. The jpeg 2000 still image compression standard. IEEE Signal processing magazine, 18(5):36–58, 2001.
  47. Privacy-preserving and verifiable src-based face recognition with cloud/edge server assistance. Computers & Security, 118:102740, 2022.
  48. Gender-adversarial networks for face privacy preserving. IEEE Internet of Things Journal, 9(18):17568–17576, 2022.
  49. Lossy image compression with compressive autoencoders. arXiv preprint arXiv:1703.00395, 2017.
  50. Compressive privacy generative adversarial network. IEEE Trans. Inf. Forensics Secur., 15:2499–2513, 2020.
  51. Paul Voigt and Axel Von dem Bussche. The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing, 10(3152676):10–5555, 2017.
  52. Gregory K. Wallace. The JPEG still picture compression standard. Commun. ACM, 34(4):30–44, 1991.
  53. Cosface: Large margin cosine loss for deep face recognition. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 5265–5274. Computer Vision Foundation / IEEE Computer Society, 2018.
  54. Mitigate bias in face recognition using skewness-aware reinforcement learning. arXiv, 2019.
  55. Deep face recognition: A survey. Neurocomputing, 429:215–244, 2021.
  56. Privacy-preserving face recognition in the frequency domain. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pages 2558–2566. AAAI Press, 2022.
  57. Privacy-preserving adversarial facial features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8212–8221, 2023.
  58. Identitydp: Differential private identification protection for face images. Neurocomputing, 501:197–211, 2022.
  59. IARPA janus benchmark-b face dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2017, Honolulu, HI, USA, July 21-26, 2017, pages 592–600. IEEE Computer Society, 2017.
  60. Privacy-preserving face recognition with outsourced computation. Soft Comput., 20(9):3735–3744, 2016.
  61. Learning in the frequency domain. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 1737–1746. Computer Vision Foundation / IEEE, 2020.
  62. A review of homomorphic encryption for privacy-preserving biometrics. Sensors, 23(7):3566, 2023.
  63. Efficient and privacy-preserving online face recognition over encrypted outsourced data. In IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), iThings/GreenCom/CPSCom/SmartData 2018, Halifax, NS, Canada, July 30 - August 3, 2018, pages 366–373. IEEE, 2018.
  64. Pro-face: A generic framework for privacy-preserving recognizable obfuscation of face images. In Proceedings of the 30th ACM International Conference on Multimedia, pages 1661–1669, 2022.
  65. A privacy-preserving multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. Frontiers Neurorobotics, 13:112, 2019.
  66. Priface: a privacy-preserving face recognition framework under untrusted server. Journal of Ambient Intelligence and Humanized Computing, 14(3):2967–2979, 2023.
  67. Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments. Technical Report 18-01, Beijing University of Posts and Telecommunications, 2018.
  68. Cross-age LFW: A database for studying cross-age face recognition in unconstrained environments. arXiv, 2017.
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Authors (9)
  1. Yuxi Mi (10 papers)
  2. Zhizhou Zhong (8 papers)
  3. Yuge Huang (18 papers)
  4. Jiazhen Ji (5 papers)
  5. Jianqing Xu (11 papers)
  6. Jun Wang (991 papers)
  7. Shaoming Wang (7 papers)
  8. Shouhong Ding (90 papers)
  9. Shuigeng Zhou (81 papers)
Citations (7)
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