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

• Published in: ACM transactions on multimedia computing communications and applications 2025-02, Vol.21 (2), p.1-23, Article 62
• Main Authors: Liu, Xiyao, Yang, Cundian, He, Jianbiao, Fang, Hui, Schaefer, Gerald, Zhang, Jian, Zhu, Yuesheng, Zhang, Shichao
• Format: Article
• Language: English
• Subjects: Digital rights management; Security and privacy
• ISSN: 1551-6857; 1551-6865
• DOI: 10.1145/3702230

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 article, we propose a novel attack-defending contrastive learning zero-watermarking (ADCL-ZW) scheme 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 watermarking 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 is that 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.