Invisible Watermarking for Audio Generation Diffusion Models
- URL: http://arxiv.org/abs/2309.13166v2
- Date: Tue, 31 Oct 2023 20:46:09 GMT
- Title: Invisible Watermarking for Audio Generation Diffusion Models
- Authors: Xirong Cao, Xiang Li, Divyesh Jadav, Yanzhao Wu, Zhehui Chen, Chen
Zeng, Wenqi Wei
- Abstract summary: This paper presents the first watermarking technique applied to audio diffusion models trained on mel-spectrograms.
Our model excels not only in benign audio generation, but also incorporates an invisible watermarking trigger mechanism for model verification.
- Score: 11.901028740065662
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Diffusion models have gained prominence in the image domain for their
capabilities in data generation and transformation, achieving state-of-the-art
performance in various tasks in both image and audio domains. In the rapidly
evolving field of audio-based machine learning, safeguarding model integrity
and establishing data copyright are of paramount importance. This paper
presents the first watermarking technique applied to audio diffusion models
trained on mel-spectrograms. This offers a novel approach to the aforementioned
challenges. Our model excels not only in benign audio generation, but also
incorporates an invisible watermarking trigger mechanism for model
verification. This watermark trigger serves as a protective layer, enabling the
identification of model ownership and ensuring its integrity. Through extensive
experiments, we demonstrate that invisible watermark triggers can effectively
protect against unauthorized modifications while maintaining high utility in
benign audio generation tasks.
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