Towards Face Encryption by Generating Adversarial Identity Masks

Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, Hui Xue; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 3897-3907


As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention. Several attempts have been made to alleviate the leakage of identity information from face photos, with the aid of, e.g., image obfuscation techniques. However, most of the present results are either perceptually unsatisfactory or ineffective against face recognition systems. Our goal in this paper is to develop a technique that can encrypt the personal photos such that they can protect users from unauthorized face recognition systems but remain visually identical to the original version for human beings. To achieve this, we propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks which can be overlaid on facial images, such that the original identities can be concealed without sacrificing the visual quality. Extensive experiments demonstrate that TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models under practical open-set test scenarios. Besides, we also show the practical and effective applicability of our method on a commercial API service.

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@InProceedings{Yang_2021_ICCV, author = {Yang, Xiao and Dong, Yinpeng and Pang, Tianyu and Su, Hang and Zhu, Jun and Chen, Yuefeng and Xue, Hui}, title = {Towards Face Encryption by Generating Adversarial Identity Masks}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {3897-3907} }