MerkleSpeech: Public-Key Verifiable, Chunk-Localised Speech Provenance via Perceptual Fingerprints and Merkle Commitments
Abstract
Speech provenance goes beyond detecting whether a watermark is present. Real workflows involve splicing, quoting, trimming, and platform-level transforms that may preserve some regions while altering others. Neural watermarking systems have made strides in robustness and localised detection, but most deployments produce outputs with no third-party verifiable cryptographic proof tying a time segment to an issuer-signed original. Provenance standards like C2PA adopt signed manifests and Merkle-based fragment validation, yet their bindings target encoded assets and break under re-encoding or routine processing. We propose MerkleSpeech, a system for public-key verifiable, chunk-localised speech provenance offering two tiers of assurance. The first, a robust watermark attribution layer (WM-only), survives common distribution transforms and answers "was this chunk issued by a known party?". The second, a strict cryptographic integrity layer (MSv1), verifies Merkle inclusion of the chunk's fingerprint under an issuer signature. The system computes perceptual fingerprints over short speech chunks, commits them in a Merkle tree whose root is signed with an issuer key, and embeds a compact in-band watermark payload carrying a random content identifier and chunk metadata sufficient to retrieve Merkle inclusion proofs from a repository. Once the payload is extracted, all subsequent verification steps (signature check, fingerprint recomputation, Merkle inclusion) use only public information. The result is a splice-aware timeline indicating which regions pass each tier and why any given region fails. We describe the protocol, provide pseudocode, and present experiments targeting very low false positive rates under resampling, bandpass filtering, and additive noise, informed by recent audits identifying neural codecs as a major stressor for post-hoc audio watermarks.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents MerkleSpeech, a system for public-key verifiable, chunk-localised speech provenance that effectively combines perceptual fingerprints and cryptographic techniques to ensure audio authenticity. Its innovative approach and rigorous evaluation contribute meaningfully to the fields of machine learning and audio forensics, addressing critical challenges in speech provenance.
Methodology
The paper introduces MerkleSpeech, a novel system for public-key verifiable, chunk-localised speech provenance. It combines perceptual fingerprinting with Merkle tree commitments to establish a two-tier verification system. The first tier offers robust watermark attribution, while the second tier provides strict cryptographic integrity verification. The methodology is well-structured, detailing the components involved, including deterministic chunk fingerprints, issuer signatures, and in-band watermark payloads. The approach is innovative, addressing the limitations of existing provenance standards that fail under typical audio processing transformations.
Results & evaluation
The experiments utilize the LibriSpeech dataset and are rigorously designed to evaluate both the robustness of the watermarking system and the accuracy of the verification process. The results demonstrate high verification rates (99.9%) on clean audio, with detailed metrics on false positive rates and robustness against various audio transformations. The paper effectively communicates the experimental setup, including the evaluation metrics and robustness suite, which enhances the credibility of the findings.
Reproducibility
The paper provides sufficient implementation details, including pseudocode for both enrollment and verification processes. However, there is no direct link to a code repository or demo, which limits the ease of reproducibility. The methodology is clear, but the absence of a publicly accessible implementation may hinder broader adoption and validation by the community.
Limitations
The authors acknowledge several limitations, including the non-collision-resistant nature of perceptual fingerprints and the lack of evaluation against neural codecs, which are known to challenge watermarking systems. Additionally, the dependency on a repository for public verification and potential vulnerabilities to white-box attacks are noted as areas for future improvement.
Broader impact
This work has significant implications for combating misinformation and fraud in audio content, particularly as speech generation technologies advance. By providing a robust framework for speech provenance, it could enhance accountability and trust in audio communications across various applications, from media to legal contexts. The focus on public-key verification aligns with growing concerns over digital authenticity and integrity. The paper presents MerkleSpeech, a system for public-key verifiable, chunk-localised speech provenance that effectively combines perceptual fingerprints and cryptographic techniques to ensure audio authenticity. Its innovative approach and rigorous evaluation contribute meaningfully to the fields of machine learning and audio forensics, addressing critical challenges in speech provenance.
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