A Robust Watermark-Based RAE Attack for Privacy and Copyright Protection of Medical Images in IoMT

In the context of the Internet of Medical Things (IoMT), medical images are highly vulnerable to unauthorized analysis and recognition by deep neural networks (DNNs) during transmission and sharing, posing serious threats to patient privacy and the rights of legitimate users. Reversible Adversarial Examples (RAEs) have been proposed to prevent unauthorized DNNs from interpreting image content without affecting normal usage by authorized parties. However, existing RAE methods primarily focus on privacy protection while largely neglecting copyright issues. To address this issue, we propose a robust RAE generation algorithm based on digital watermarking, which achieves copyright protection and reversible adversarial protection. Specifically, LayerCAM is used to determine the watermark embedding region (WR), and a multi-objective optimization method with dynamic weight adjustment named as DW-NSGA-PSO is presented to optimize the embedding position and transparency. Then, the alpha blending strategy is adopted to fuse the watermark into the original sample, generating a watermarked adversarial example. Next, the watermarked image with location information serves as the base sample, and optimized adversarial perturbations are embedded into the WR to resist removal attack of watermark removal networks. Finally, reversible data hiding (RDH) is used to embed information about the WR into the non-embedding regions of the image, enabling reversible adversarial protection. Besides, our algorithm further enhances the transfer attack capability of RAEs and robustness against various image attacks by randomly preprocessing the images. Experimental results demonstrate that the proposed algorithm achieves significantly attack performance on both color and grayscale image datasets, reaching a maximum ASR of 91%, and it may effectively protect both privacy and copyright across multiple datasets while maintaining good visual quality.