{"ID":2861079,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03387","arxiv_id":"2510.03387","title":"Synthetic Audio Forensics Evaluation (SAFE) Challenge","abstract":"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/}.","short_abstract":"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 evalua...","url_abs":"https://arxiv.org/abs/2510.03387","url_pdf":"https://arxiv.org/pdf/2510.03387v2","authors":"[\"Kirill Trapeznikov\",\"Paul Cummer\",\"Pranay Pherwani\",\"Jai Aslam\",\"Michael S. Davinroy\",\"Peter Bautista\",\"Laura Cassani\",\"Matthew Stamm\",\"Jill Crisman\"]","published":"2025-10-03T17:48:57Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"eess.AS\"]","methods":"[]","has_code":false}