Robust Zero-watermarking for medical image security using residual-based GAN

The rapid advancement of digital technology has enhanced medical image transmission and storage; yet network openness and anonymity pose risks to copyright and privacy. Given the limitations of existing zero-watermarking schemes in dealing with complex geometric attacks, we propose a robust zero-watermarking scheme that leverages a residual-based GAN (Res-GAN). Our scheme enhances the security of the watermark image by scrambling it with a logistic chaotic map and extracts robust features from medical images by harnessing a pretrained Res-GAN model. These features are converted into a binary vector via the average hash algorithm, which is then XORed with the scrambled watermark image to generate the zero-watermark. Experimental results highlight that integrating residual modules into GAN boosts the scheme’s robustness against geometric attacks. Under various attacks, our scheme can still maintain the normalized correlation coefficient values above 0.84. Compared with other existing zero-watermarking schemes for medical images, our scheme has stronger robustness. This scheme offers effective technical support and practical guidance for medical image copyright and privacy protection. It enhances the security of medical image transmission and sharing, fostering data circulation and cooperation. As medical informatization advances, this technology will be pivotal in medical image management, telemedicine, and big data analysis.