  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.
