A Robust Image Watermarking Scheme via Two-Stage Training and Differentiable JPEG Compression
Hu Deng, Feng Chen, Pei Gan, Rongtao Liao, Xuehu Yan · Electronics · 2025
Digital image watermarking is a vital tool for copyright protection and content authentication. However, most existing methods perform well only under single noise types, while real-world applications often involve composite noises with multiple distortions, leading to poor robustness. To address this issue, we propose a robust image watermarking scheme. To improve performance under combined noise conditions, a two-stage training strategy is introduced: in the first stage, noise intensity increases gradually to stabilize training; in the second stage, mixed strong noises are applied to enhance generalization against complex attacks. Specifically, a strength-balanced watermark optimization algorithm is employed during the testing stage to improve visual quality while maintaining strong robustness. Furthermore, to improve robustness against JPEG compression, we adopt a differentiable fine-grained JPEG module that accurately simulates real compression and enables gradient backpropagation during training. Experimental results demonstrate the superiority of the proposed method under various single and combined distortions. Under noise-free conditions, it achieves 0% bit error rate and 53.55 dB PSNR. Under composite distortions, our scheme maintains a low average BER of 2.40% and a PSNR of 42.70 dB.