Two-Stage Separable Adversarial Distortion-Based Robust Watermarking Framework for Diffusion Tensor Imaging

Recent deep learning-based watermarking methods have achieved impressive results. However, they struggle with unknown distortions and often suffer from poor generalization, slow convergence, unstable training, and degraded visual quality in watermarked images. To address the above problems, this paper proposes a two-stage separable adversarial distortion (TSAD)-based robust watermarking algorithm for diffusion tensor imaging (DTI). The algorithm uses a noise-free end-to-end network in the first stage for learning and training DTI images. In the second stage, it fixes the watermark embedding network trained in the first stage, interacts the noise distortion network with the watermark extraction network to perform adversarial training for improving robustness. Experimental results show that our method achieves comparable or better robustness to seen distortions and better robustness to unseen distortions, along with enhanced stability, faster convergence, and improved visual quality in watermarked DTI images.