Speech watermarking techniques can proactively mitigate the potential harmful consequences of instant voice cloning techniques. These techniques involve the insertion of signals into speech that are imperceptible to humans but can be detected by algorithms. Previous approaches typically embed watermark messages into continuous space. However, intuitively, embedding watermark information into robust discrete latent space can significantly improve the robustness of watermarking systems. In this paper, we propose DiscreteWM, a novel speech watermarking framework that injects watermarks into the discrete intermediate representations of speech. Specifically, we map speech into discrete latent space with a vector-quantized autoencoder and inject watermarks by changing the modular arithmetic relation of discrete IDs. To ensure the imperceptibility of watermarks, we also propose a manipulator model to select the candidate tokens for watermark embedding. Experimental results demonstrate that our framework achieves state-of-the-art performance in robustness and imperceptibility, simultaneously. Moreover, our flexible frame-wise approach can serve as an efficient solution for both voice cloning detection and information hiding. Additionally, DiscreteWM can encode 1 to 150 bits of watermark information within a 1-second speech clip, indicating its encoding capacity. Audio samples are available at https://DiscreteWM.github.io/discrete_wm.

翻译：语音水印技术能够主动缓解即时语音克隆技术可能带来的潜在危害。这类技术将人类难以察觉但算法可检测的信号嵌入语音中。以往方法通常将水印信息嵌入连续空间。然而，直观而言，将水印信息嵌入鲁棒的离散潜在空间能显著提升水印系统的稳健性。本文提出DiscreteWM——一种将水印注入语音离散中间表示的新型语音水印框架。具体而言，我们通过矢量量化自编码器将语音映射至离散潜在空间，并通过改变离散ID的模运算关系来嵌入水印。为确保水印的不可感知性，我们还提出一种操纵器模型来筛选适合水印嵌入的候选标记。实验结果表明，该框架在鲁棒性与不可感知性方面均达到最先进水平。此外，我们灵活的帧级处理方法可同时为语音克隆检测和信息隐藏提供高效解决方案。DiscreteWM能在1秒语音片段中编码1至150比特水印信息，这证明了其编码容量。音频样本详见https://DiscreteWM.github.io/discrete_wm。