/face_deidentification

some paper and website for face de-identification

MIT LicenseMIT

Awesome Face De-identification

Papers：

  • Samarzija B, Ribaric S. An approach to the de-identification of faces in different poses[C]//2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2014: 1246-1251.

  • Rafique M A, Azam M S, Jeon M, et al. Face-deidentification in images using restricted boltzmann machines[C]//2016 11th International Conference for Internet Technology and Secured Transactions (ICITST). IEEE, 2016: 69-73.

  • Marčetić D, Samaržija B, Soldić M, et al. Face de-identification for privacy protection in surveillance systems[C]//ROSUS2016. 2016.

  • Meden B, Mallı R C, Fabijan S, et al. Face deidentification with generative deep neural networks[J]. IET Signal Processing, 2017, 11(9): 1046-1054

  • Meng L, Sun Z, Collado O T. Efficient approach to de-identifying faces in videos[J]. IET Signal Processing, 2017, 11(9): 1039-1045.

  • Wu Y, Yang F, Ling H. Privacy-protective-gan for face de-identification[J]. arXiv preprint arXiv:1806.08906, 2018.

  • Meden B, Emeršič Ž, Štruc V, et al. k-Same-Net: k-Anonymity with generative deep neural networks for face deidentification[J]. Entropy, 2018, 20(1): 60.

  • Wu Y, Yang F, Xu Y, et al. Privacy-protective-GAN for privacy preserving face de-identification[J]. Journal of Computer Science and Technology, 2019, 34(1): 47-60.

  • Bailer W, Winter M. On Improving Face Generation for Privacy Preservation[C]//2019 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2019: 1-6.

  • Hao H, Güera D, Reibman A R, et al. A Utility-Preserving GAN for Face Obscuration[J]. arXiv preprint arXiv:1906.11979, 2019

  • Hao H, Güera D, Horváth J, et al. Robustness Analysis of Face Obscuration[J]. arXiv preprint arXiv:1905.05243, 2019.

  • Chatzikyriakidis E, Papaioannidis C, Pitas I. Adversarial Face De-Identification[C]//2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019: 684-688.

  • Li Y, Lyu S. De-identification without losing faces[C]//Proceedings of the ACM Workshop on Information Hiding and Multimedia Security. 2019: 83-88.

  • Gafni O, Wolf L, Taigman Y. Live face de-identification in video[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 9378-9387. blog video remark

  • Yang J, Liu J, Wu J. Facial Image Privacy Protection Based on Principal Components of Adversarial Segmented Image Blocks[J]. IEEE Access, 2020, 8: 103385-103394.

  • Mirjalili V, Raschka S, Ross A. PrivacyNet: semi-adversarial networks for multi-attribute face privacy[J]. arXiv preprint arXiv:2001.00561, 2020.

  • Cho D, Lee J H, Suh I H. CLEANIR: Controllable Attribute-Preserving Natural Identity Remover[J]. Applied Sciences, 2020, 10(3): 1120.

  • Yang L, Liu C, Wang P, et al. HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment[J]. arXiv preprint arXiv:2005.05005, 2020 project

  • Proença H. The UU-Net: Reversible Face De-Identification for Visual Surveillance Video Footage[J]. arXiv preprint arXiv:2007.04316, 2020. code blog

  • Yang X, Dong Y, Pang T, et al. Towards Privacy Protection by Generating Adversarial Identity Masks [J]. arXiv preprint arXiv:2003.06814, 2020.

survey

face swap

website：

researcher