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

Convolutional neural networks learns spatial features and are heavily
interlinked within kernels. The SE module have broken the traditional route of
neural networks passing the entire result to next layer. Instead SE only passes
important features to be learned with its squeeze and excitation (SE) module.
We propose variations of the SE module which improvises the process of squeeze
and excitation and enhances the performance. The proposed squeezing or exciting
the layer makes it possible for having a smooth transition of layer weights.
These proposed variations also retain the characteristics of SE module. The
experimented results are carried out on residual networks and the results are
tabulated.

本文提出了 SE 模块的变体，通过改进挤压和兴奋的过程，实现了平滑的层权重过渡和强化性能，在残差网络上进行实验的结果表明，这些变体保留了 SE 模块的特性。