Picking watermarks from noise (PWFN): an improved robust watermarking model against intensive distortions
This addresses robustness limitations in digital watermarking for applications requiring protection against strong noise attacks, representing an incremental improvement over existing encoder-noise-decoder architectures.
The paper tackles the problem of improving robustness in deep learning-based watermarking against intensive noise distortions by introducing a denoise module between the noise layer and decoder, along with an SE module for better information fusion. Experimental results show the method outperforms state-of-the-art models under different noise intensities.
Digital watermarking is the process of embedding secret information by altering images in an undetectable way to the human eye. To increase the robustness of the model, many deep learning-based watermarking methods use the encoder-noise-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 decoder against stronger noise, this paper proposes to introduce a denoise module between the noise layer and the decoder. The module aims to reduce noise and recover some of the information lost caused by distortion. 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.