Shields for Digital Images: A Watermarking Method with KAN Block and Simulation-Enhanced Noise Pool to Resist Screen-Camera Attacks

To address the issues of privacy leakage and copyright infringement in screen-camera scenarios, we propose a robust image watermarking method, which incorporates kolmogorov–arnold network (KAN) blocks and a simulation-enhanced noise pool to resist screen-camera noise attacks. Specifically, we first modify the traditional convolutional blocks for processing high-dimensional features in the U-Net-based encoder to KAN blocks. This operation enhances the ability of encoder to model nonlinear relationships between complex features, while preserving global structure of the original image and minimizing the damage to local details caused by watermark embedding, thereby improving visual quality of the watermarked image. Additionally, to enhance the robustness of the proposed method against complex screen-camera noise attacks, a simulation-enhanced noise pool containing mathematical models and a deep noise simulation network, called NSim-Net, is designed. Especially, in NSim-Net, adversarial training between the simulator based on the improved U-Net and the discriminator based on PatchGAN effectively improves the ability to simulate complex noise. Experimental results demonstrate that, compared to typical screen-camera resilient watermarking methods, the watermarked image generated by the proposed method achieves a maximum peak signal-to-noise ratio (PSNR) improvement of 4.78 dB. Furthermore, based on our simulation-enhanced noise pool, the watermark extraction accuracy of the proposed method exceeds 98% under various screen-camera noise attacks.