GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis
<|reference_start|>GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis: Amid the burgeoning development of generative models like diffusion models, the task of differentiating synthesized audio from its natural counterpart grows more daunting. Deepfake detection offers a viable solution to combat this challenge. Yet, this defensive measure unintentionally fuels the continued refinement of generative models. Watermarking emerges as a proactive and sustainable tactic, preemptively regulating the creation and dissemination of synthesized content. Thus, this paper, as a pioneer, proposes the generative robust audio watermarking method (Groot), presenting a paradigm for proactively supervising the synthesized audio and its source diffusion models. In this paradigm, the processes of watermark generation and audio synthesis occur simultaneously, facilitated by parameter-fixed diffusion models equipped with a dedicated encoder. The watermark embedded within the audio can subsequently be retrieved by a lightweight decoder. The experimental results highlight Groot's outstanding performance, particularly in terms of robustness, surpassing that of the leading state-of-the-art methods. Beyond its impressive resilience against individual post-processing attacks, Groot exhibits exceptional robustness when facing compound attacks, maintaining an average watermark extraction accuracy of around 95%.<|reference_end|>
@article{liu2024groot:,
    title={GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio
  Synthesis},
    author={Weizhi Liu, Yue Li, Dongdong Lin, Hui Tian and Haizhou Li},
    journal={arXiv preprint arXiv:2407.10471},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10471},
    primaryClass={cs.CR cs.AI cs.SD eess.AS}
}