RBGAN: Realistic-generation and balanced-utility GAN for face de-identification

Abstract

The rise of computer vision technologies and surveillance devices has led to numerous studies on vision, but it also raises privacy concerns, especially regarding facial identity information. Protecting the privacy of face identity information has prompted the development of face de-identification, which faces two main challenges. The first challenge is the realism of the generated images, while the second challenge is finding the proper balance between privacy protection and preserving data utility. To address these challenges, we propose a face de-identification model called Realistic-Generation and Balanced-Utility GAN (RBGAN). RBGAN includes a disentangled module and a symmetric-consistency-guided generation module, which aim to preserve multiple attributes while generating realistic de-identified images. We design an attribute-specialized encoder to obtain the disentangled representation of identity and attribute to balance privacy protection and data utility preservation. To generate realistic de-identified images, we incorporate symmetric consistency and face reconstruction into the face de-identification process using the Generative Adversarial Network (GAN). The disentangled module can extract richer representation, and the symmetric-consistency-guided generation module can construct more facial details. As a result, our proposed RBGAN can generate de-identified face images that closely resemble real faces and are difficult to detect by visual systems. Experiments conducted on benchmark face datasets demonstrate the effectiveness of our model in generating realistic images while preserving multiple attributes.