Massive captured face images are stored in the database for the identification of individuals. However, the stored images can be observed intentionally or unintentionally by data managers, which is not at the will of individuals and may cause privacy violations. Existing protection works only slightly change the visual content of the face while maintaining the utility of identification, making it susceptible to the inference of the true identity by human vision. In this paper, we propose an identity hider that enables significant visual content change for human vision while preserving high identifiability for face recognizers. Firstly, the identity hider generates a virtual face with new visual content by manipulating the latent space in StyleGAN2. In particular, the virtual face has the same irrelevant attributes as the original face, e.g., pose and expression. Secondly, the visual content of the virtual face is transferred into the original face and then the background is replaced with the original one. In addition, the identity hider has strong transferability, which ensures an arbitrary face recognizer can achieve satisfactory accuracy. Adequate experiments show that the proposed identity hider achieves excellent performance on privacy protection and identifiability preservation.

翻译：海量人脸图像存储于数据库中以供个体身份识别。然而，存储的图像可能被数据管理者有意或无意地观察，这违背了个体意愿并可能导致隐私侵犯。现有保护方法仅轻微改变人脸视觉内容，同时保持识别功能的有效性，使其易受人类视觉推断真实身份的攻击。本文提出一种隐身份器（identity hider），能在显著改变人眼视觉内容的同时，为人脸识别器保留高识别性能。首先，该隐身份器通过操控StyleGAN2中的潜在空间生成具有全新视觉内容的虚拟人脸。特别地，该虚拟人脸保留了与原始人脸相同的无关属性（如姿态和表情）。其次，将虚拟人脸的视觉内容迁移至原始人脸，并将原始背景替换回图像中。此外，该隐身份器具备强迁移性，可确保任意人脸识别器均能达到满意精度。充分的实验表明，所提隐身份器在隐私保护与识别性能保留方面均表现优异。