Synthetic Audio Forensics Evaluation (SAFE) Challenge
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909828323999744 |
|---|---|
| author | Trapeznikov, Kirill Cummer, Paul Pherwani, Pranay Aslam, Jai Davinroy, Michael S. Bautista, Peter Cassani, Laura Stamm, Matthew Crisman, Jill |
| author_facet | Trapeznikov, Kirill Cummer, Paul Pherwani, Pranay Aslam, Jai Davinroy, Michael S. Bautista, Peter Cassani, Laura Stamm, Matthew Crisman, Jill |
| contents | The increasing realism of synthetic speech generated by advanced text-to-speech (TTS) models, coupled with post-processing and laundering techniques, presents a significant challenge for audio forensic detection. In this paper, we introduce the SAFE (Synthetic Audio Forensics Evaluation) Challenge, a fully blind evaluation framework designed to benchmark detection models across progressively harder scenarios: raw synthetic speech, processed audio (e.g., compression, resampling), and laundered audio intended to evade forensic analysis. The SAFE challenge consisted of a total of 90 hours of audio and 21,000 audio samples split across 21 different real sources and 17 different TTS models and 3 tasks. We present the challenge, evaluation design and tasks, dataset details, and initial insights into the strengths and limitations of current approaches, offering a foundation for advancing synthetic audio detection research. More information is available at \href{https://stresearch.github.io/SAFE/}{https://stresearch.github.io/SAFE/}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03387 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synthetic Audio Forensics Evaluation (SAFE) Challenge Trapeznikov, Kirill Cummer, Paul Pherwani, Pranay Aslam, Jai Davinroy, Michael S. Bautista, Peter Cassani, Laura Stamm, Matthew Crisman, Jill Sound Audio and Speech Processing The increasing realism of synthetic speech generated by advanced text-to-speech (TTS) models, coupled with post-processing and laundering techniques, presents a significant challenge for audio forensic detection. In this paper, we introduce the SAFE (Synthetic Audio Forensics Evaluation) Challenge, a fully blind evaluation framework designed to benchmark detection models across progressively harder scenarios: raw synthetic speech, processed audio (e.g., compression, resampling), and laundered audio intended to evade forensic analysis. The SAFE challenge consisted of a total of 90 hours of audio and 21,000 audio samples split across 21 different real sources and 17 different TTS models and 3 tasks. We present the challenge, evaluation design and tasks, dataset details, and initial insights into the strengths and limitations of current approaches, offering a foundation for advancing synthetic audio detection research. More information is available at \href{https://stresearch.github.io/SAFE/}{https://stresearch.github.io/SAFE/}. |
| title | Synthetic Audio Forensics Evaluation (SAFE) Challenge |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.03387 |