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

TL;DR

This paper presents an enhanced deep learning-based watermarking model that incorporates a denoise module and SE module to significantly improve robustness against strong noise distortions.

Contribution

The paper introduces a denoise module and SE module into the watermarking architecture, enhancing robustness and efficiency compared to existing methods.

Findings

Outperforms state-of-the-art models under various noise levels

Denoise module effectively recovers lost information from distortions

SE module improves encoder efficiency and watermark fusion

Abstract

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.