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Backward Compatibility in Zero-Bit Neural Audio Watermarking

Paper recorded by Signals 4 on 2026-10-15 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.LG · 机器学习 · first seen 2026-10-16

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

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#269 most recent of 749 cs.LG papers we have recorded · ↑ newer: Robust Reinforcement Learning for Dynamic Treatment Regimes under Mode · ↓ older: Towards Continual Learning: Mitigating Forgetting With Subspace Tuning
Cite this page: Backward Compatibility in Zero-Bit Neural Audio Watermarking: the #269 most recent of 749 cs.LG papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/backward-compatibility-in-zero-bit-neural-audio-watermarking.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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