基于单张图像的个性化面部隐私保护
Personalized Face Privacy Protection From a Single Image
AI总结：
本文提出FaceCloak系统，通过单张图像生成个性化面部隐私掩码，有效防止面部识别，经实验验证其在多个数据集上优于其他方法。
摘要：
在线上传的面部照片容易受到恶意行为者的攻击，他们可以刮取面部图像并通过未经授权的面部识别模型侵犯个人隐私。本文提出了FaceCloak，一种新颖的个性化面部隐私保护系统，该系统能够从用户单张图像生成防御性身份特定的通用面部隐私掩码，使面部识别失败。FaceCloak引入了三阶段的个性化面部扰动学习方法：(1)基于用户的单张图像生成少量高多样性的合成面部图像；(2)通过迭代扰动生成在合成图像的小集合上学习面部伪装，通过增加关键面部身份泄露区域的保护，有效将用户的身份嵌入推向遥远的锚身份并远离相似身份；(3)生成以像素级伪装形式的个性化身份保护掩码，该掩码轻量且可以高效应用于任何用户的面部图像，同时保持良好的感知质量。在三个流行面部数据集上对十个识别模型的广泛实验显示，FaceCloak相比29种其他现有代表性方法更有效。代码可在https://github.com/zacharyyahn/FaceCloak获取。
英文摘要：
Photos of faces uploaded online are vulnerable to malicious actors who can scrape facial images from online sources and intrude on personal privacy via unauthorized use of facial recognition models. This paper presents FaceCloak, a novel personalized face privacy protection system, which can generate defensive identity-specific universal face privacy masks from a single image of a user, causing facial recognition to fail. FaceCloak introduces a three-stage personalized face perturbation learning methodology: (1) It generates a small set of high-variety synthetic face images of a person based on a single image of the person. (2) It learns face cloaking by adding more protection to key facial-identity leakage regions through iterative perturbation generation over the small set of synthetic images, effectively shifting a user's identity embedding towards a distant anchor identity and away from a similar one. (3) It generates a personalized identity-protective mask in the form of pixel-wise cloaking, which is light-weight and can be efficiently applied to any facial image of a user while maintaining good perceptual quality. Extensive experiments on three popular face datasets across ten recognition models show the effectiveness of FaceCloak compared to 29 other existing representative methods. Code is available at https://github.com/zacharyyahn/FaceCloak
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。
