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arXiv 2405.05170cs.MMcs.CVeess.IV

从噪声中提取水印(PWFN):一种抗强失真的改进鲁棒水印模型

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

Huazhong University of Science and Technology(华中科技大学);Nanchang University(南昌大学)

Sijing Xie, Chengxin Zhao, Nan Sun, Wei Li, Hefei Ling

AI总结:

针对现有深度学习水印方法仅能抗弱噪声的问题,本文提出PWFN模型,通过在噪声层与解码器间加去噪模块、引入SE模块提升编解码效率,在不同噪声强度下性能优于现有最优模型。

摘要:

数字水印是通过人眼无法察觉的方式修改图像以嵌入秘密信息的过程。为提升模型鲁棒性,许多基于深度学习的水印方法采用编码器-噪声-解码器架构,在噪声层添加不同类型的噪声,随后解码器从失真图像中提取水印信息。但该方法仅能抵御弱噪声攻击。为增强解码器对更强噪声的抵抗能力,本文提出在噪声层与解码器之间引入去噪模块,该模块旨在降低噪声并恢复部分因失真丢失的信息。此外,本文引入SE(Squeeze-and-Excitation,压缩与激励)模块,从像素维度和通道维度融合水印信息,提升编码器效率。实验结果表明,本文提出的方法与现有模型性能相当,且在不同噪声强度下优于当前最优(state-of-the-art)模型。消融实验也验证了所提模块的优越性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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