Open-Set Source Tracing of Audio Deepfake Systems
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908441673465856 |
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| author | Klein, Nicholas Tak, Hemlata Khoury, Elie |
| author_facet | Klein, Nicholas Tak, Hemlata Khoury, Elie |
| contents | Existing research on source tracing of audio deepfake systems has focused primarily on the closed-set scenario, while studies that evaluate open-set performance are limited to a small number of unseen systems. Due to the large number of emerging audio deepfake systems, robust open-set source tracing is critical. We leverage the protocol of the Interspeech 2025 special session on source tracing to evaluate methods for improving open-set source tracing performance. We introduce a novel adaptation to the energy score for out-of-distribution (OOD) detection, softmax energy (SME). We find that replacing the typical temperature-scaled energy score with SME provides a relative average improvement of 31% in the standard FPR95 (false positive rate at true positive rate of 95%) measure. We further explore SME-guided training as well as copy synthesis, codec, and reverberation augmentations, yielding an FPR95 of 8.3%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06470 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open-Set Source Tracing of Audio Deepfake Systems Klein, Nicholas Tak, Hemlata Khoury, Elie Audio and Speech Processing Sound Existing research on source tracing of audio deepfake systems has focused primarily on the closed-set scenario, while studies that evaluate open-set performance are limited to a small number of unseen systems. Due to the large number of emerging audio deepfake systems, robust open-set source tracing is critical. We leverage the protocol of the Interspeech 2025 special session on source tracing to evaluate methods for improving open-set source tracing performance. We introduce a novel adaptation to the energy score for out-of-distribution (OOD) detection, softmax energy (SME). We find that replacing the typical temperature-scaled energy score with SME provides a relative average improvement of 31% in the standard FPR95 (false positive rate at true positive rate of 95%) measure. We further explore SME-guided training as well as copy synthesis, codec, and reverberation augmentations, yielding an FPR95 of 8.3%. |
| title | Open-Set Source Tracing of Audio Deepfake Systems |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2507.06470 |