Advancing Zero-Shot Open-Set Speech Deepfake Source Tracing
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| Format: | Preprint |
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2025
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| _version_ | 1866915999453806592 |
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| author | Chhibber, Manasi Mishra, Jagabandhu Kinnunen, Tomi H. |
| author_facet | Chhibber, Manasi Mishra, Jagabandhu Kinnunen, Tomi H. |
| contents | We propose a novel zero-shot source tracing framework inspired by speaker verification. We adapt SSL-AASIST for attack classification, enhancing embeddings with AAM loss and RegMixup, and ensure that training attacks are disjoint from those forming fingerprint-trial pairs. For backend scoring in attack verification, we explore both zero-shot approaches (cosine similarity and Siamese) and few-shot approaches (MLP and Siamese). Experiments on our recently introduced STOPA dataset with an open set setting show that few-shot learning provides advantages in the in-distribution (ID) scenario, while zero-shot approaches perform better in the out-of-distribution (OOD) scenario. In attack source verification with ID trials, few-shot Siamese and MLP achieve equal error rates (EER) of 17.72% and 13.11%, compared to 29.91% for zero-shot cosine scoring. Conversely, in OOD trials, zero-shot cosine scoring reaches 16.43%, outperforming few-shot Siamese at 23.47% and MLP at 21.57%. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_24674 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Zero-Shot Open-Set Speech Deepfake Source Tracing Chhibber, Manasi Mishra, Jagabandhu Kinnunen, Tomi H. Audio and Speech Processing We propose a novel zero-shot source tracing framework inspired by speaker verification. We adapt SSL-AASIST for attack classification, enhancing embeddings with AAM loss and RegMixup, and ensure that training attacks are disjoint from those forming fingerprint-trial pairs. For backend scoring in attack verification, we explore both zero-shot approaches (cosine similarity and Siamese) and few-shot approaches (MLP and Siamese). Experiments on our recently introduced STOPA dataset with an open set setting show that few-shot learning provides advantages in the in-distribution (ID) scenario, while zero-shot approaches perform better in the out-of-distribution (OOD) scenario. In attack source verification with ID trials, few-shot Siamese and MLP achieve equal error rates (EER) of 17.72% and 13.11%, compared to 29.91% for zero-shot cosine scoring. Conversely, in OOD trials, zero-shot cosine scoring reaches 16.43%, outperforming few-shot Siamese at 23.47% and MLP at 21.57%. |
| title | Advancing Zero-Shot Open-Set Speech Deepfake Source Tracing |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.24674 |