A Robust Image Blind Watermarking Scheme Based on Staged Adaptive Strategy

Image blind watermarking is a critical tool for copyright protection and verification of digital images. Existing watermarking schemes usually perform well under a single noise condition. However, in practical applications, watermarked images are often exposed to many different types of noise. This combined noise condition significantly degrades the image quality and watermark extraction accuracy of existing watermarking schemes. To address these challenges, we propose a novel two-stage training strategy that enhances watermarking robustness by training the model with various noise intensities, improving performance under combined noise conditions. To further improve the imperceptibility of the watermarked image while ensuring high accuracy of watermark extraction, we propose a strength balanced watermarking optimization algorithm in the model testing phase. Furthermore, due to the non-differentiable nature of JPEG compression, existing schemes cannot effectively obtain satisfactory watermarking performance for JPEG compression. We introduce a differentiable fine-grained JPEG compression module to improve the robustness of existing schemes for JPEG compression. Experimental results indicate that our proposed scheme outperforms state-of-the-art schemes under multiple noise conditions. Under noise-free condition, it achieves a 0 bit error rate and 53.55 dB PSNR, and under combined noise conditions, it still achieves an average of 2.40 bit error rate and 42.70 dB PSNR.