CochlearMark：基于锁相听觉神经表征的音频水印
CochlearMark: Audio Watermarking via Phase Locked Auditory Nerve Representations
AI总结：
本文提出无需训练的音频水印CochlearMark，利用听觉神经锁相表征优化波形扰动，在语音、音乐等音频上实现与神经基线相当的鲁棒性与不可感知性，且是唯一在神经编解码器重编码下仍具可检测性的无训练方法。
摘要：
现代音频水印通过学习得到的编解码器模型实现了强鲁棒性，但其在新处理下的表现仅能通过经验确定。本文提出CochlearMark，这是一种无需训练的音频水印，采用听觉神经锁相作为其特征表征。针对该表征的密钥控制随机投影优化了微小的波形扰动，检测时使用相同的投影，无需训练的编码器或解码器。由于锁相表征了时间精细结构，由此产生的鲁棒性具有直接的生理解释：当该结构被保留时，水印得以保留；当该结构被破坏或重建时，水印则丢失。在语音、音乐和环境音频上，CochlearMark实现了与训练过的神经基线相当的信号编辑鲁棒性和不可感知性，同时仅需轻量级计算开销。在评估的所有无需训练的方法中，CochlearMark是唯一在神经编解码器重编码下仍保持显著可检测性的方法。
英文摘要：
Modern audio watermarks achieve strong robustness with learned encoder-decoder models, but their behavior under new processing is established empirically. CochlearMark is introduced as a training-free audio watermark that uses auditory-nerve phase-locking as its feature representation. A small waveform perturbation is optimized against a keyed random projection of this representation, and the same projection is used for detection, with no trained encoder or decoder. Because phase-locking represents temporal fine structure, the resulting robustness has a direct physiological interpretation: the mark is retained when this structure is preserved and lost when it is disrupted or rebuilt. Across speech, music, and environmental audio, CochlearMark achieves signal-edit robustness and imperceptibility comparable to trained neural baselines while requiring only a lightweight computational footprint. Among the evaluated training-free methods, CochlearMark is the only one to retain substantial detectability under neural-codec re-encoding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
