Protecting Your Voice: Temporal-aware Robust Watermarking

• URL: http://arxiv.org/abs/2504.14832v2
• Date: Sat, 21 Jun 2025 15:59:07 GMT
• Title: Protecting Your Voice: Temporal-aware Robust Watermarking
• Authors: Yue Li, Weizhi Liu, Dongdong Lin, Hui Tian, Hongxia Wang,
• Abstract summary: textbfunderlinetemporal-aware textbfunderlinerobtextbfunderlineust wattextbfunderlineermarking (emphTrue)<n> integrated content-driven encoder is designed for watermarked waveform reconstruction.<n> temporal-aware gated convolutional network is meticulously designed to bit-wise recover the watermark.
• Score: 10.883912837253794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of generative models has led to the synthesis of real-fake ambiguous voices. To erase the ambiguity, embedding watermarks into the frequency-domain features of synthesized voices has become a common routine. However, the robustness achieved by choosing the frequency domain often comes at the expense of fine-grained voice features, leading to a loss of fidelity. Maximizing the comprehensive learning of time-domain features to enhance fidelity while maintaining robustness, we pioneer a \textbf{\underline{t}}emporal-aware \textbf{\underline{r}}ob\textbf{\underline{u}}st wat\textbf{\underline{e}}rmarking (\emph{True}) method for protecting the speech and singing voice. For this purpose, the integrated content-driven encoder is designed for watermarked waveform reconstruction, which is structurally lightweight. Additionally, the temporal-aware gated convolutional network is meticulously designed to bit-wise recover the watermark. Comprehensive experiments and comparisons with existing state-of-the-art methods have demonstrated the superior fidelity and vigorous robustness of the proposed \textit{True} achieving an average PESQ score of 4.63.

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