TriniMark: A Robust Generative Speech Watermarking Method for Trinity-Level Traceability
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912904937209856 |
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| author | Li, Yue Liu, Weizhi Lin, Kaiqing Lin, Dongdong Kallas, Kassem |
| author_facet | Li, Yue Liu, Weizhi Lin, Kaiqing Lin, Dongdong Kallas, Kassem |
| contents | Diffusion-based speech generation has achieved remarkable fidelity, increasing the risk of misuse and unauthorized redistribution. However, most existing generative speech watermarking methods are developed for GAN-based pipelines, and watermarking for diffusion-based speech generation remains comparatively underexplored. In addition, prior work often focuses on content-level provenance, while support for model-level and user-level attribution is less mature. We propose \textbf{TriniMark}, a diffusion-based generative speech watermarking framework that targets trinity-level traceability, i.e., the ability to associate a generated speech sample with (i) the embedded watermark message (content-level provenance), (ii) the source generative model (model-level attribution), and (iii) the end user who requested generation (user-level traceability). TriniMark uses a lightweight encoder to embed watermark bits into time-domain speech features and reconstruct the waveform, and a temporal-aware gated convolutional decoder for reliable bit recovery. We further introduce a waveform-guided fine-tuning strategy to transfer watermarking capability into a diffusion model. Finally, we incorporate variable-watermark training so that a single trained model can embed different watermark messages at inference time, enabling scalable user-level traceability. Experiments on speech datasets indicate that TriniMark maintains speech quality while improving robustness to common single and compound signal-processing attacks, and it supports high-capacity watermarking for large-scale traceability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20532 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TriniMark: A Robust Generative Speech Watermarking Method for Trinity-Level Traceability Li, Yue Liu, Weizhi Lin, Kaiqing Lin, Dongdong Kallas, Kassem Multimedia Cryptography and Security Sound Audio and Speech Processing Diffusion-based speech generation has achieved remarkable fidelity, increasing the risk of misuse and unauthorized redistribution. However, most existing generative speech watermarking methods are developed for GAN-based pipelines, and watermarking for diffusion-based speech generation remains comparatively underexplored. In addition, prior work often focuses on content-level provenance, while support for model-level and user-level attribution is less mature. We propose \textbf{TriniMark}, a diffusion-based generative speech watermarking framework that targets trinity-level traceability, i.e., the ability to associate a generated speech sample with (i) the embedded watermark message (content-level provenance), (ii) the source generative model (model-level attribution), and (iii) the end user who requested generation (user-level traceability). TriniMark uses a lightweight encoder to embed watermark bits into time-domain speech features and reconstruct the waveform, and a temporal-aware gated convolutional decoder for reliable bit recovery. We further introduce a waveform-guided fine-tuning strategy to transfer watermarking capability into a diffusion model. Finally, we incorporate variable-watermark training so that a single trained model can embed different watermark messages at inference time, enabling scalable user-level traceability. Experiments on speech datasets indicate that TriniMark maintains speech quality while improving robustness to common single and compound signal-processing attacks, and it supports high-capacity watermarking for large-scale traceability. |
| title | TriniMark: A Robust Generative Speech Watermarking Method for Trinity-Level Traceability |
| topic | Multimedia Cryptography and Security Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2504.20532 |