{"ID":2866395,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19812","arxiv_id":"2509.19812","title":"Efficient Speech Watermarking for Speech Synthesis via Progressive Knowledge Distillation","abstract":"With the rapid advancement of speech generative models, unauthorized voice cloning poses significant privacy and security risks. Speech watermarking offers a viable solution for tracing sources and preventing misuse. Current watermarking technologies fall mainly into two categories: DSP-based methods and deep learning-based methods. DSP-based methods are efficient but vulnerable to attacks, whereas deep learning-based methods offer robust protection at the expense of significantly higher computational cost. To improve the computational efficiency and enhance the robustness, we propose PKDMark, a lightweight deep learning-based speech watermarking method that leverages progressive knowledge distillation (PKD). Our approach proceeds in two stages: (1) training a high-performance teacher model using an invertible neural network-based architecture, and (2) transferring the teacher's capabilities to a compact student model through progressive knowledge distillation. This process reduces computational costs by 93.6% while maintaining high level of robust performance and imperceptibility. Experimental results demonstrate that our distilled model achieves an average detection F1 score of 99.6% with a PESQ of 4.30 in advanced distortions, enabling efficient speech watermarking for real-time speech synthesis applications.","short_abstract":"With the rapid advancement of speech generative models, unauthorized voice cloning poses significant privacy and security risks. Speech watermarking offers a viable solution for tracing sources and preventing misuse. Current watermarking technologies fall mainly into two categories: DSP-based methods and deep learning-...","url_abs":"https://arxiv.org/abs/2509.19812","url_pdf":"https://arxiv.org/pdf/2509.19812v1","authors":"[\"Yang Cui\",\"Peter Pan\",\"Lei He\",\"Sheng Zhao\"]","published":"2025-09-24T06:52:14Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.MM\",\"eess.AS\"]","methods":"[]","has_code":false}