  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.
