SPEECH & AUDIO

Proactive Detection of Voice Cloning with Localized Watermarking

June 05, 2024

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

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AUTHORS

Written by

Robin San Romin

Pierre Fernandez

Hady Elsahar

Alexandre Deffosez

Teddy Furon

Tuan Tran

Publisher

ICML

Research Topics

Speech & Audio

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