  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%.
