Neural Codec Source Tracing: Toward Comprehensive Attribution in Open-Set Condition
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915099052081152 |
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| author | Xie, Yuankun Wang, Xiaopeng Wang, Zhiyong Fu, Ruibo Wen, Zhengqi Cao, Songjun Ma, Long Li, Chenxing Cheng, Haonnan Ye, Long |
| author_facet | Xie, Yuankun Wang, Xiaopeng Wang, Zhiyong Fu, Ruibo Wen, Zhengqi Cao, Songjun Ma, Long Li, Chenxing Cheng, Haonnan Ye, Long |
| contents | Current research in audio deepfake detection is gradually transitioning from binary classification to multi-class tasks, referred as audio deepfake source tracing task. However, existing studies on source tracing consider only closed-set scenarios and have not considered the challenges posed by open-set conditions. In this paper, we define the Neural Codec Source Tracing (NCST) task, which is capable of performing open-set neural codec classification and interpretable ALM detection. Specifically, we constructed the ST-Codecfake dataset for the NCST task, which includes bilingual audio samples generated by 11 state-of-the-art neural codec methods and ALM-based out-ofdistribution (OOD) test samples. Furthermore, we establish a comprehensive source tracing benchmark to assess NCST models in open-set conditions. The experimental results reveal that although the NCST models perform well in in-distribution (ID) classification and OOD detection, they lack robustness in classifying unseen real audio. The ST-codecfake dataset and code are available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Codec Source Tracing: Toward Comprehensive Attribution in Open-Set Condition Xie, Yuankun Wang, Xiaopeng Wang, Zhiyong Fu, Ruibo Wen, Zhengqi Cao, Songjun Ma, Long Li, Chenxing Cheng, Haonnan Ye, Long Sound Artificial Intelligence Audio and Speech Processing Current research in audio deepfake detection is gradually transitioning from binary classification to multi-class tasks, referred as audio deepfake source tracing task. However, existing studies on source tracing consider only closed-set scenarios and have not considered the challenges posed by open-set conditions. In this paper, we define the Neural Codec Source Tracing (NCST) task, which is capable of performing open-set neural codec classification and interpretable ALM detection. Specifically, we constructed the ST-Codecfake dataset for the NCST task, which includes bilingual audio samples generated by 11 state-of-the-art neural codec methods and ALM-based out-ofdistribution (OOD) test samples. Furthermore, we establish a comprehensive source tracing benchmark to assess NCST models in open-set conditions. The experimental results reveal that although the NCST models perform well in in-distribution (ID) classification and OOD detection, they lack robustness in classifying unseen real audio. The ST-codecfake dataset and code are available. |
| title | Neural Codec Source Tracing: Toward Comprehensive Attribution in Open-Set Condition |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.06514 |