Privacy-Protective-GAN for Face De-identification

TL;DR

This paper introduces Privacy-Protective-GAN (PP-GAN), a novel face de-identification framework using GANs with specialized modules to enhance privacy, visual quality, and structure preservation, outperforming existing methods.

Contribution

The paper presents a new GAN-based framework with verificator and regulator modules specifically designed for face de-identification, improving privacy protection and visual quality.

Findings

Outperforms existing face de-identification techniques

Retains structure similarity while ensuring privacy

Provides a practical GAN-based framework with domain priors

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.