With extensive face images being shared on social media, there has been a
notable escalation in privacy concerns. In this paper, we propose AdvCloak, an
innovative framework for privacy protection using generative models. AdvCloak
is designed to automatically customize class-wise adversarial masks that can
maintain superior image-level naturalness while providing enhanced
feature-level generalization ability. Specifically, AdvCloak sequentially
optimizes the generative adversarial networks by employing a two-stage training
strategy. This strategy initially focuses on adapting the masks to the unique
individual faces via image-specific training and then enhances their
feature-level generalization ability to diverse facial variations of
individuals via person-specific training. To fully utilize the limited training
data, we combine AdvCloak with several general geometric modeling methods, to
better describe the feature subspace of source identities. Extensive
quantitative and qualitative evaluations on both common and celebrity datasets
demonstrate that AdvCloak outperforms existing state-of-the-art methods in
terms of efficiency and effectiveness.

基于生成模型的隐私保护创新框架 AdvCloak 通过自动定制类别级对抗性遮蔽模型，在保持图像级自然性的同时提供改进的特征级泛化能力，进而解决社交媒体中广泛共享面部图像所带来的隐私问题。通过两阶段训练策略，这一框架不仅能够个性化适应独特的个人面部特征，还能提高面对个体间的面部差异时的特征级概括能力，并结合几种通用的几何建模方法来更好地描述源身份的特征子空间。此外，对常见和名人数据集进行的广泛定量和定性评估结果表明，AdvCloak 在效率和效果方面优于现有的最先进方法。