Recovering lost watermarks using image denoising

Paper Title:

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

Published on:

8 May 2024

Primary Category:

Multimedia

Paper Authors:

Sijing Xie,

Chengxin Zhao,

Nan Sun,

Wei Li,

Hefei Ling

Bullets

Key Details

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Introduces denoising module to recover lost watermarks after noise layer

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Adds SE module to encoder to fuse watermark pixel and channel-wise

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Achieves state-of-the-art robustness under high noise levels

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Matches performance of existing methods under low noise

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Ablations validate superiority of proposed modules

Explore the topics in this paper

denoising

encoder decoder architecture

image watermarking

neural networks

robustness

AI generated summary

Recovering lost watermarks using image denoising

This paper proposes a robust image watermarking model that introduces a denoising module between the noise layer and decoder in the typical encoder-decoder architecture. The denoising module reduces noise and recovers watermark information lost during attacks, improving robustness. Additionally, a SE module is added to the encoder to fuse watermarking information pixel and channel-wise, enhancing efficiency. Experiments show the model matches or exceeds state-of-the-art methods under high noise levels. Ablations demonstrate the value of each proposed component.