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New framework formalizes backward compatibility for neural audio watermarking

By PulseAugur Editorial · [1 sources] ·

Researchers have formalized the concept of backward compatibility in zero-bit neural audio watermarking, addressing issues where updated embedders and detectors can break detection or increase false positives. A new training framework pairs detectors with frozen legacy embedders, using a performance-weighted sampling strategy to maintain accuracy across versions. To combat embedder-collapse and preserve the true negative rate against unrecognized embedders, an Angular Diversity Loss (ADL) is introduced, forcing new embedders into distinct output space regions without sacrificing detection accuracy or audio quality. AI

IMPACT Introduces a method to improve the robustness and security of neural audio watermarking systems against updates.

RANK_REASON Academic paper detailing a new method for neural audio watermarking. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework formalizes backward compatibility for neural audio watermarking

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Academic paper detailing a new method for neural audio watermarking. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Admir Krni\'c, Kosta Pavlovi\'c, Petar Nedi\'c, \v{Z}eljko Bolevi\'c, Slavko Kova\v{c}evi\'c, Igor Djurovi\'c ·

    Backward Compatibility in Zero-Bit Neural Audio Watermarking

    arXiv:2610.22296v1 Announce Type: cross 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 decoup…

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