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AudioSeal

Neural audio watermarking with sample-level detection of AI speech.

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About

AudioSeal is Meta's audio watermarking system for proactive detection of AI-generated speech. It jointly trains a generator that embeds an imperceptible watermark signal into a waveform and a detector that returns a per-sample probability of the watermark being present. That localization is the key innovation: instead of a binary watermarked-or-not verdict, it can pinpoint which segments of a longer clip, such as a partially voice-cloned podcast, were synthesized.

Presented at ICML 2024, it uses a perceptual loss inspired by auditory masking and a fast single-pass detector that runs orders of magnitude faster than prior approaches. Meta released the code and weights publicly with a commercial-use license, positioning it as infrastructure for platforms fighting voice-cloning scams and misinformation. Independent evaluations note that, like all post-hoc audio watermarks, it can be degraded by neural codecs and denoisers, so it is a layer of defense rather than a guarantee.

Specs

Context window

Pricing

Free quota

API access

Weights

Open weights

Released

June 18, 2024

Benchmarks

Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.

Use cases

watermarkingdeepfake-detectionaudio-forensicscontent-authenticity

Trust score — 7/10

/10 trustMetrics-checked

Scored 7/10 (metrics tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 83; Trending score 68/100 from researcher assessment.

Sources