GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis

Weizhi Liu, Yue Li, Dongdong Lin, Hui Tian, Haizhou Li

Abstract: 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%.

Primary Subject Area: [Generation] Social Aspects of Generative AI

Relevance To Conference: Our work focuses on the audio domain within multimedia. Specifically, proposing a method where audio inherently contains specific watermark information during the generation process. More precisely, our proposed generative audio watermarking approach involves embedding the watermark as input into the generative model, enabling the generation of audio containing the watermark.

Submission Number: 1178