个性化隐私保护面具以对抗未经授权的人脸识别
Personalized Privacy Protection Mask Against Unauthorized Facial Recognition
AI总结：
本文提出Chameleon方法，为每个用户生成个性化隐私保护面具P3-Mask，以对抗未经授权的人脸识别，兼具高效保护、保持图像质量及对未知模型的鲁棒性，并在基准测试中优于现有方法。
摘要：
人脸识别（FR）技术可能被滥用以侵犯隐私。政府、私营公司甚至个人攻击者可以通过网络抓取收集人脸图像，构建一个未经个人同意即可识别其身份的人脸识别系统。本文介绍了Chameleon，它学习生成一种以用户为中心的个性化隐私保护面具，称为P3-Mask，以保护人脸图像免受未经授权的人脸识别攻击，并具有三个显著特点。首先，我们使用跨图像优化为每个用户生成一个P3-Mask，而不是为用户的每张人脸图像定制扰动。这使得即使对于计算资源有限的用户也能实现高效且即时的保护。其次，我们引入了可感知性优化，以保持受保护人脸图像的视觉质量。第三，我们通过将焦点多样性优化的集成学习整合到面具生成过程中，增强了P3-Mask对未知人脸识别模型的鲁棒性。在两个基准数据集上的大量实验表明，Chameleon在即时保护和最小图像质量退化方面优于三种最先进的方法。此外，Chameleon利用P3-Mask作为个性化的去混淆密钥，实现了成本效益高的人脸识别授权，并展现出对自适应对手的高抵抗力。
英文摘要：
Face recognition (FR) can be abused for privacy intrusion. Governments, private companies, or even individual attackers can collect facial images by web scraping to build an FR system identifying human faces without their consent. This paper introduces Chameleon, which learns to generate a user-centric personalized privacy protection mask, coined as P3-Mask, to protect facial images against unauthorized FR with three salient features. First, we use a cross-image optimization to generate one P3-Mask for each user instead of tailoring facial perturbation for each facial image of a user. It enables efficient and instant protection even for users with limited computing resources. Second, we incorporate a perceptibility optimization to preserve the visual quality of the protected facial images. Third, we strengthen the robustness of P3-Mask against unknown FR models by integrating focal diversity-optimized ensemble learning into the mask generation process. Extensive experiments on two benchmark datasets show that Chameleon outperforms three state-of-the-art methods with instant protection and minimal degradation of image quality. Furthermore, Chameleon enables cost-effective FR authorization using the P3-Mask as a personalized de-obfuscation key, and it demonstrates high resilience against adaptive adversaries.
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。
