GenRAN: GenFusion-guided Reversible Anonymization Network for face privacy preserving

Abstract

The rapid advancement of social networks has made it possible to obtain personal face images without permission. While recent advances in face privacy preservation focus on anonymizing facial features, their effectiveness is limited by challenges in achieving high fidelity for both anonymized and recovered faces in practical scenarios. To address these challenges, we introduce GenFusion, which incorporates Virtual Face Generation (VFG) into the Bi-branch Fusion process with coupling reversibility. Accordingly, we propose a GenFusion-based Reversible Anonymization Network (GenRAN) for enhanced face privacy preservation. Our approach integrates a Multi-Fusion (MF) module, enabling an anonymization encoder to create natural and realistic anonymized faces by fusing original images with virtual faces generated through the VFG module. Furthermore, high-fidelity recovery of the original face from the anonymized version is achieved via an anonymization decoder, which employs a Multi-Recovery module that shares unified parameters with the MF module. Additionally, we introduce a Feature Mixing guided Dense Block to adaptively blend original facial details into the anonymized images, maintain high realism across varying image types. Extensive experiments show that proposed GenRAN can achieve better performance on actual privacy preserving scenarios, while obtaining high perceptual fidelity of anonymized and recovered faces than SOTA methods.