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About this paper
Weizhi Liu, Yue Li, Dongdong Lin, Hui Tian, Haizhou Li
GROOT introduces a generative robust audio watermarking method for diffusion-model-based audio synthesis, embedding watermarks directly during content generation. This technique maintains high audio fidelity and extraction accuracy, achieving over 90% accuracy against complex compound attacks, which exceeds the robustness of existing audio watermarking approaches.
Key insights
- Directly manipulating the diffusion model's latent space with an encoded watermark allows for robust embedding during generation, moving beyond reactive detection.
- The decoder's architecture, specifically employing Modified Gated Convolutional Neural Networks (MGCNNs), is crucial for achieving high watermark extraction accuracy.
- Fixing the pre-trained diffusion model parameters allows for a 'plug-and-play' solution, enabling easy integration without extensive model retraining while preserving generative quality.
Summary by alphaXiv.
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