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.

本文提出了一种改进的数字水印算法，通过在噪声层和解码器之间引入去噪模块来增强该模型对于更强噪声的鲁棒性，同时引入 SE 模块以提高编码器的效率。实验结果表明该方法在不同噪声强度下可以媲美现有模型并且胜过同类型算法，消融实验进一步验证了我们提出的模块的优越性。

从噪音中挑选水印（PWFN）：一种针对强度扭曲的改进鲁棒性水印模型

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

Learning-based methods have attracted a lot of research attention and led to
significant improvements in low-light image enhancement. However, most of them
still suffer from two main problems: expensive computational cost in high
resolution images and unsatisfactory performance in simultaneous enhancement
and denoising. To address these problems, we propose BDCE, a bootstrap
diffusion model that exploits the learning of the distribution of the curve
parameters instead of the normal-light image itself. Specifically, we adopt the
curve estimation method to handle the high-resolution images, where the curve
parameters are estimated by our bootstrap diffusion model. In addition, a
denoise module is applied in each iteration of curve adjustment to denoise the
intermediate enhanced result of each iteration. We evaluate BDCE on commonly
used benchmark datasets, and extensive experiments show that it achieves
state-of-the-art qualitative and quantitative performance.

使用 BDCE，一种基于学习的曲线参数分布估计模型，来解决高分辨率图像的计算成本高和同时增强和去噪效果不佳的问题，并通过大量实验证明其在常用基准数据集上达到了最先进的定性和定量性能。