Massive captured face images are stored in the database for the identification of individuals. However, these 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 schemes can maintain identifiability but slightly change the facial appearance, rendering it still susceptible to the visual perception of the original identity by data managers. In this paper, we propose an effective identity hider for human vision protection, which can significantly change appearance to visually hide identity while allowing identification for face recognizers. Concretely, the identity hider benefits from two specially designed modules: 1) The virtual face generation module generates a virtual face with a new appearance by manipulating the latent space of StyleGAN2. In particular, the virtual face has a similar parsing map to the original face, supporting other vision tasks such as head pose detection. 2) The appearance transfer module transfers the appearance of the virtual face into the original face via attribute replacement. Meanwhile, identity information can be preserved well with the help of the disentanglement networks. In addition, diversity and background preservation are supported to meet the various requirements. Extensive experiments demonstrate that the proposed identity hider achieves excellent performance on privacy protection and identifiability preservation.

翻译：大量采集的人脸图像被存储在数据库中用于个体识别。然而，这些图像可能被数据管理者有意或无意地观察，这并非个体意愿且可能导致隐私侵犯。现有保护方案虽能维持可识别性，但会轻微改变面部外观，使其仍易被数据管理者通过视觉感知原始身份。本文提出一种有效的人类视觉保护身份隐藏器，该方案能显著改变外观以视觉上隐藏身份，同时保持人脸识别器对身份的识别能力。具体而言，身份隐藏器受益于两个专门设计的模块：1）虚拟人脸生成模块通过操控StyleGAN2的潜在空间生成具有新外观的虚拟人脸。特别地，虚拟人脸与原始人脸具有相似的解析图，支持头部姿态检测等其他视觉任务。2）外观迁移模块通过属性替换将虚拟人脸的外观迁移至原始人脸。同时，借助解耦网络可良好保留身份信息。此外，该方案支持多样性和背景保留以满足不同需求。大量实验证明，所提出的身份隐藏器在隐私保护和可识别性保留方面均表现出色。