一种面向身份保持人脸匿名化的密钥驱动框架
A Key-Driven Framework for Identity-Preserving Face Anonymization
AI总结：
本文提出密钥驱动的人脸匿名化与认证识别框架KFAAR，通过HPVFG生成保持头部姿态和表情的虚拟人脸，并利用KVFA在正确密钥下恢复原始身份，同时实现隐私保护与身份可识别。
摘要：
虚拟人脸是元宇宙中的关键内容。近年来，已有研究尝试生成用于隐私保护的虚拟人脸。然而，这些虚拟人脸要么永久移除可识别信息，要么将原始身份映射为另一个虚拟身份，从而永久丢失原始身份。在本研究中，我们首次尝试解决虚拟人脸中隐私性与可识别性之间的冲突，并提出了一个密钥驱动的人脸匿名化与认证识别（KFAAR）框架。具体而言，KFAAR框架由一个头部姿态保持的虚拟人脸生成（HPVFG）模块和一个密钥可控的虚拟人脸认证（KVFA）模块组成。HPVFG模块利用用户密钥将原始人脸的隐向量投影为虚拟隐向量，然后将虚拟向量映射以获得扩展编码，并基于该编码生成虚拟人脸。通过同时加入头部姿态和面部表情校正模块，虚拟人脸具有与原始人脸相同的头部姿态和面部表情。在认证过程中，我们提出KVFA模块，在使用正确用户密钥的条件下直接识别虚拟人脸，从而在不暴露原始人脸图像的情况下获得原始身份。我们还提出了一个多任务学习目标来训练HPVFG和KVFA。大量实验证明了所提出的HPVFG和KVFA模块的优势，能够有效同时实现人脸匿名性和可识别性。
英文摘要：
Virtual faces are crucial content in the metaverse. Recently, attempts have been made to generate virtual faces for privacy protection. Nevertheless, these virtual faces either permanently remove the identifiable information or map the original identity into a virtual one, which loses the original identity forever. In this study, we first attempt to address the conflict between privacy and identifiability in virtual faces, where a key-driven face anonymization and authentication recognition (KFAAR) framework is proposed. Concretely, the KFAAR framework consists of a head posture-preserving virtual face generation (HPVFG) module and a key-controllable virtual face authentication (KVFA) module. The HPVFG module uses a user key to project the latent vector of the original face into a virtual one. Then it maps the virtual vectors to obtain an extended encoding, based on which the virtual face is generated. By simultaneously adding a head posture and facial expression correction module, the virtual face has the same head posture and facial expression as the original face. During the authentication, we propose a KVFA module to directly recognize the virtual faces using the correct user key, which can obtain the original identity without exposing the original face image. We also propose a multi-task learning objective to train HPVFG and KVFA. Extensive experiments demonstrate the advantages of the proposed HPVFG and KVFA modules, which effectively achieve both facial anonymity and identifiability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
