用一张自拍照生成专属防护面具，让人脸识别失效。
Personalized Face Privacy Protection From a Single Image
- 从单张照片生成多样化人脸图像，用于训练防护模型。
- 通过迭代扰动聚焦关键特征区，使身份嵌入远离真实身份。
- 生成轻量级像素级遮罩，保护效果好且视觉质量高。
网上上传的人脸照片易被恶意获取，用于未经授权的面部识别，侵犯隐私。本文提出FaceCloak，一种个性化人脸隐私保护系统，仅需用户一张照片即可生成专属的通用防护面具，使人脸识别失败。FaceCloak采用三阶段个性化扰动学习：(1) 基于单张照片生成一组高多样性的合成人脸图像；(2) 通过在合成图像上迭代生成扰动，强化对关键身份泄露区域的保护，有效将用户的身份嵌入推向远距离锚点身份，远离相似身份；(3) 生成轻量级像素级遮罩，可高效应用于任意用户人脸图像，同时保持良好视觉质量。在三个主流人脸数据集上，针对十种识别模型的大量实验表明，FaceCloak优于29种现有方法。代码已开源：https://github.com/zacharyyahn/FaceCloak。
原文摘要 · Abstract (English)
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
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