Backward Compatibility in Zero-Bit Neural Audio Watermarking
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 performan...
| Authors | Admir Krnić, Kosta Pavlović, Petar Nedić et al. |
|---|---|
| Primary category | cs.SD |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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