Beyond Privacy: Generating Privacy-Preserving Faces Supporting Robust Image Authentication

Tao Wang, Wenying Wen, Xiangli Xiao, Zhongyun Hua, Yushu Zhang, Yuming Fang

Abstract: The prevalence of face capturing along with the advancement of face recognition poses a potential threat to individual privacy. To protect privacy, plenty of methods have been proposed to change identity in the face, thus blocking malicious face recognition. However, these methods fail to satisfy authentication requirements for special application scenarios, e.g., face authentication in surveillance capture. In this paper, we propose a novel face privacy protection model, which supports robust image authentication via information-conditional identity transformation. Specifically, we first introduce a basic face manipulation model (FMM), which can preserve identity-irrelevant attributes when manipulating identity. Based on FMM, we further design a lightweight protector called AIDPro, outputting a transformed identity which is different from the original one and is embedded a message presenting authentication information. Benefiting from the semantic robustness, our model does not require noise layers to achieve accurate message extraction after various image distortions. In addition, the message can be the condition to guide the identity transformation for privacy protection, which avoids extra resource consumption from supporting image authentication. Extensive experimental results demonstrate our model has comparable privacy protection performance, superior attribute preservation performance, and robust authentication performance especially in JPEG compression and screen shooting. Our code is available at https://github.com/daizigege/AIDPro.