Picking watermarks from noise (PWFN): an improved robust watermarking model against intensive distortions

Digital watermarking is the process of embedding secret information by altering images in a way that is undetectable to the human eye. To increase the robustness of the model, many deep learning-based watermarking methods use the encoder-decoder architecture by adding different noises to the noise layer. The decoder then extracts the watermarked information from the distorted image. However, this method can only resist weak noise attacks. To improve the robustness of the algorithm against stronger noise, this paper proposes to introduce a denoise module between the noise layer and the decoder. The module is aimed at reducing noise and recovering some of the information lost during an attack. Additionally, the paper introduces the SE module to fuse the watermarking information pixel-wise and channel dimensions-wise, improving the encoder's efficiency. Experimental results show that our proposed method is comparable to existing models and outperforms state-of-the-art under different noise intensities. In addition, ablation experiments show the superiority of our proposed module.