Attack-Defending Contrastive Learning for Volumetric Medical Image Zero-Watermarking

Abstract

Zero-watermarking is an emerging distortion-free copyright protection method for volumetric medical images. However, achieving both robustness against various malicious attacks and distinguishability between individual images remains challenging. In this paper, we propose a novel attack-defending contrastive learning zero-watermarking scheme (ADCL-ZW) to tackle the above challenge using deep learning-based representations. In our approach, we design an attack-defending data enrichment mechanism to enhance the watermarking robustness by generating a large number of image samples under various watermarking attacks. Subsequently, features for both watermarking distinguishability and robustness are enhanced through application of a contrastive loss. In particular, we implement a dual-stream Siamese network architecture to effectively handle both signal attacks and geometric attacks in order to enhance the wartermarking performance. Experimental results demonstrate that ADCL-ZW achieves stronger watermarking robustness and a better tradeoff between watermarking robustness and distinguishability compared with state-of-the art zero-watermarking methods. One of the highlighted metrics the false negative rate of ADCL-ZW achieves 0.01 when a fixed false positive rate is set to 1%, which is more than 13.3 times better than the benchmark methods.