Proactive Detection of Voice Cloning with Localized Watermarking
Robin San Roman, Pierre Fernandez, Hady Elsahar, Alexandre Défossez, Teddy Furon, Tuan Tran
Abstract
In the rapidly evolving field of speech generative models, there is a pressing need to ensure audio authenticity against the risks of voice cloning. We present AudioSeal, the first audio watermarking technique designed specifically for localized detection of AI-generated speech. AudioSeal employs a generator / detector architecture trained jointly with a localization loss to enable localized watermark detection up to the sample level, and a novel perceptual loss inspired by auditory masking, that enables AudioSeal to achieve better imperceptibility. AudioSeal achieves state-of-the-art performance in terms of robustness to real life audio manipulations and imperceptibility based on automatic and human evaluation metrics. Additionally, AudioSeal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed, achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications.Code is available at https://github.com/facebookresearch/audioseal
BibTeX
@inproceedings{
roman2024proactive,
title={Proactive Detection of Voice Cloning with Localized Watermarking},
author={Robin San Roman and Pierre Fernandez and Hady Elsahar and Alexandre D{\'e}fossez and Teddy Furon and Tuan Tran},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=Bic3Vmy2DG}
}