Face de-identification has become increasingly important as the image sources
are explosively growing and easily accessible. The advance of new face
recognition techniques also arises people's concern regarding the privacy
leakage. The mainstream pipelines of face de-identification are mostly based on
the k-same framework, which bears critiques of low effectiveness and poor
visual quality. In this paper, we propose a new framework called
Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and
regulator modules specially designed for the face de-identification problem to
ensure generating de-identified output with retained structure similarity
according to a single input. We evaluate the proposed approach in terms of
privacy protection, utility preservation, and structure similarity. Our
approach not only outperforms existing face de-identification techniques but
also provides a practical framework of adapting GAN with priors of domain
knowledge.

提出了一种基于隐私保护 GAN（PP-GAN）的新框架，将具有新颖真实性验证器和调节器模块的 GAN 用于人脸去识别问题，以确保在保持结构相似度的情况下产生去识别输出。该方法在隐私保护、实用保护和结构相似度方面的表现优于现有的人脸去识别技术，并提供了一种通过领域知识先验来封装 GAN 框架的实用解决方案。