Privacy-Protective-GAN for Face De-identification

Abstract

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.

References in corpus (4)

• Adam: A Method for Stochastic Optimization
• Conditional Generative Adversarial Nets
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• A Light CNN for Deep Face Representation with Noisy Labels

Cited by in corpus (8)

• GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
• Security and Privacy Approaches in Mixed Reality: A Literature Survey
• Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging
• Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
• AnonymousNet: Natural Face De-Identification with Measurable Privacy
• Live Face De-Identification in Video
• Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest
• An Assessment of GANs for Identity-related Applications