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WavMark: Watermarking for Audio Generation
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Recent breakthroughs in zero-shot voice synthesis have enabled imitating a speaker's voice using just a few seconds of recording while maintaining a high level of realism. Alongside its potential benefits, this powerful technology introduces notable risks, including voice fraud and speaker impersonation. Unlike the conventional approach of solely relying on passive methods for detecting synthetic data, watermarking presents a proactive and robust defence mechanism against these looming risks. This paper introduces an innovative audio watermarking framework that encodes up to 32 bits of watermark within a mere 1-second audio snippet. The watermark is imperceptible to human senses and exhibits strong resilience against various attacks. It can serve as an effective identifier for synthesized voices and holds potential for broader applications in audio copyright protection. Moreover, this framework boasts high flexibility, allowing for the combination of multiple watermark segments to achieve heightened robustness and expanded capacity. Utilizing 10 to 20-second audio as the host, our approach demonstrates an average Bit Error Rate (BER) of 0.48\% across ten common attacks, a remarkable reduction of over 2800\% in BER compared to the state-of-the-art watermarking tool. See https://aka.ms/wavmark for demos of our work.
Forward citations
Cited by 12 Pith papers
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MusicMark: A Robust Generative Watermarking Framework for Music Generation
Embedding watermark bits into diffusion semantic latents via a frozen-backbone adapter yields far more robust music provenance than post-hoc watermarking under codecs and cover-song attacks.
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Investigating Codec-Internal Latent Audio Watermarking for Neural Codec Robustness
Embedding watermarks inside a codec-like autoencoder's continuous latent space improves EnCodec-24k bit accuracy to ~95–97%, but the gain is in-distribution and does not transfer to EnCodec-16k.
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SSTMark: Robust Training-Free Semantic-Level Speech Watermarking
SSTMark embeds a watermark in AI speech by rewriting its transcript and resynthesizing it, achieving strong average robustness to audio distortions but at the cost of altering the spoken content.
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Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking
Major watermarking benchmarks omit cross-lingual, cultural, and demographic reporting, creating a pluralistic evaluation gap that current governance mandates ignore.
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Making Separation-First Multi-Stream Audio Watermarking Feasible via Joint Training
Jointly training the watermark embedder/detector with the source separator enables ~1% bit-error-rate recovery of per-stem watermarks after mixing and separation, where independent training yields 15–35%.
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WaveVerify: A Novel Audio Watermarking Framework for Media Authentication and Combatting Deepfakes
WaveVerify embeds audio watermarks with a FiLM-based generator and extracts them with a Mixture-of-Experts detector, reporting zero bit error and high localization under common distortions.
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TalkLess: Blending Extractive and Abstractive Speech Summarization for Editing Speech to Preserve Content and Style
TalkLess blends extractive and abstractive speech summarization through LLM candidate generation and a weighted scoring function, then converts transcript edits to audio with VoiceCraft, evaluating favorably against a...
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A Comparative Study on Proactive and Passive Detection of Deepfake Speech
A unified evaluation framework shows watermarking models perfectly separate real from fake speech in a clean lab setup, but all four tested defenses lose accuracy under channel noise, codecs, and pitch changes.
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VoiceMark: Zero-Shot Voice Cloning-Resistant Watermarking Approach Leveraging Speaker-Specific Latents
VoiceMark embeds watermarks in speaker-specific codec latents and detects them with over 95% accuracy after zero-shot voice cloning, versus about 50% for prior methods.
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Traceable TTS: Toward Watermark-Free TTS with Strong Traceability
A joint training loop makes an F5-TTS model produce audio that a paired wav2vec 2.0/LCNN discriminator can recognize, enabling watermark-free attribution; however, the reported generalization gain is not isolated from...
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De-AntiFake: Rethinking the Protective Perturbations Against Voice Cloning Attacks
Existing voice-protection perturbations succeed only against naive attackers; a phoneme-guided purification-refinement pipeline restores cloneability of protected speech for most VC models.
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WAKE: Watermarking Audio with Key Enrichment
WAKE embeds and decodes multiple 32-bit audio watermarks with separate 8-bit keys using an invertible neural network, avoiding the overwriting problem in existing systems.
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