用于对神经编解码器重合成具有鲁棒性的潜在音频水印

中文摘要 AI 辅助

现有的波形域音频水印对许多传统失真具有鲁棒性，但在神经编解码器重合成下可能会显著退化。我们研究了连续神经编解码器潜在表示是否提供了一个更合适的嵌入空间，使用围绕冻结的预训练EnCodec构建的受限公式。为了测试这一点，一个前馈嵌入器将多比特负载映射为加性潜在扰动，并通过未更改的编解码器解码器进行解码。与AudioSeal和WavMark相比，我们的潜在水印公式在重复和低比特率EnCodec重合成下退化更缓慢，可迁移到未见过的DAC，并在大多数波形失真下保持高检测率。即使没有编解码器重合成监督，显著的EnCodec鲁棒性也会出现，表明这种行为是潜在公式固有的，并通过编解码器感知训练得到进一步加强。学习到的扰动在编解码器循环中比等范数随机对照保留得更强，且保留程度更多取决于通道特定分配而非时间结构。端到端感知质量接近冻结EnCodec重建的质量，表明观察到的退化大部分源于编解码器载体本身。总体而言，这些结果表明连续神经编解码器潜在表示为水印提供了一个有前景的嵌入空间，使其对神经编解码器重合成保持鲁棒性。

英文摘要

Existing waveform-domain audio watermarks are robust to many conventional distortions but can degrade substantially under neural codec resynthesis. We investigate whether continuous neural codec latents provide a more suitable embedding space using a restricted formulation built around frozen pretrained EnCodec. To test this, a feedforward embedder maps a multi-bit payload to an additive latent perturbation decoded through the unchanged codec decoder. Compared with AudioSeal and WavMark, our latent watermark formulation degrades more gradually under repeated and low-bitrate EnCodec resynthesis, transfers to unseen DAC, and retains high detection under most waveform distortions. Substantial EnCodec robustness emerges even without codec-resynthesis supervision, indicating that this behavior is inherent to our latent formulation and is further strengthened by codec-aware training. Learned perturbations are also preserved more strongly through codec cycling than equal-norm random controls, with preservation depending more on channel-specific allocation than temporal structure. End-to-end perceptual quality remains close to that of the frozen EnCodec reconstruction, indicating that much of the observed degradation originates from the codec carrier itself. Overall, these results show that continuous neural codec latents provide a promising embedding space for watermarks that remain robust to neural codec resynthesis.

发表机构

• Munich Music Labs（慕尼黑音乐实验室）
• Technical University of Munich（慕尼黑工业大学）

机构由 AI 辅助整理，请以论文原文为准。