A Comprehensive Image Protection Framework Based on High-Capacity Adversarial Data Hiding

In recent years, a large number of personal images have been uploaded to social network platforms, contributing to the formation of image Big Data. These images are vulnerable to security threats, e.g., privacy inference, copyright infringement, and malicious tampering. Many image protection methods have been proposed, e.g., privacy protection methods based on adversarial perturbations, and copyright protection methods based on data hiding. However, these methods can only deal with a single security threat causing the image to suffer from residual threats. Therefore, this paper proposes a comprehensive image protection framework, which generates adversarial examples by embedding meaningful perturbations, achieving image privacy protection while protecting copyright and integrity by data hiding. In this framework, we design a novel high-capacity adversarial data hiding model (HADH) to support adversarially embedding of adequate robust watermarking for copyright protection and fragile watermarking for integrity protection. Experimental results show that HADH achieves a high embedding rate of 3.21 Reed-Solomon bits per pixel (RS-bpp), providing sufficient capacity for copyright and identity verification information. The capacity is even higher than other pure robust steganography schemes. In addition, the privacy protection performance is better than the existing adversarial attack-based privacy protection methods.