RA-RevGAN: region-aware reversible adversarial example generation network for privacy-preserving applications

Abstract

The rise of online sharing platforms has provided people with diverse and convenient ways to share images. However, a substantial amount of sensitive user information is contained within these images, which can be easily captured by malicious neural networks. To ensure the secure utilization of authorized protected data, reversible adversarial attack techniques have emerged. Existing algorithms for generating adversarial examples do not strike a good balance between visibility and attack capability. Additionally, the network oscillations generated during the training process affect the quality of the final examples. To address these shortcomings, we propose a novel reversible adversarial network based on generative adversarial networks (RA-RevGAN). In this paper, the generator is used for noise generation to map features into perturbations of the image, while the region selection module confines these perturbations to specific areas that significantly affect classification. Furthermore, a robust attack mechanism is integrated into the discriminator to stabilize the network’s training by optimizing convergence speed and minimizing time cost. Extensive experiments have demonstrated that the proposed method ensures a high image generation rate, excellent attack capability, and superior visual quality while maintaining high classification accuracy in image restoration.