Backward Compatibility in Zero-Bit Neural Audio Watermarking

Abstract:Neural watermarking systems are typically trained as tightly coupled embedder-detector pairs and implicitly assume static deployment. In real-world deployments, embedders and detectors are frequently updated and selectively decoupled for security and versioning purposes, which can break detection in previously embedded content and, more critically, increase false-positive rates on embedders that a given detector is intentionally not intended to recognize. In this paper the concept of backward compatibility in zero-bit audio watermarking is formalized. A training framework is proposed that pairs a new detector with a sequence of frozen legacy embedders (its legacy chain), using a performance-weighted sampling strategy so that the detector retains accuracy across all earlier versions. This coupling, while necessary for backward compatibility, is shown to induce an embedder-collapse effect that catastrophically degrades the true negative rate (TNR) against out-of-chain embedders. To counteract this, a Angular Diversity Loss (ADL) is introduced that explicitly forces each new embedder to occupy a distinct region of the output space. The combined approach restores near-perfect TNR with no measurable cost to detection accuracy or perceptual audio quality.

Sound (cs.SD); Machine Learning (cs.LG); Multimedia (cs.MM)

arXiv:2610.22296 [cs.SD]

(or arXiv:2610.22296v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2610.22296 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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