# Synthetic Audio Forensics Evaluation (SAFE) Challenge

arXiv: https://arxiv.org/abs/2510.03387 · HTML: https://mlsanity.com/paper/2510.03387/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2025.

## Built on by (most cited first, 2 of 2)

- [Deepfake Word Detection by Next-token Prediction using Fine-tuned Whisper](https://mlsanity.com/paper/2602.22658/) (2026)
- [A SUPERB-Style Benchmark of Self-Supervised Speech Models for Audio Deepfake Detection](https://mlsanity.com/paper/2603.01482/) (2026)

## Abstract (arXiv)

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 hrefhttps://stresearch.github.io/SAFE/https://stresearch.github.io/SAFE/.