Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models
| Type of publication: | Journal paper |
| Citation: | Dowerah_IEEE_OJSP_2026 |
| Publication status: | Published |
| Journal: | IEEE Open Journal of Signal Processing |
| Volume: | 7 |
| Year: | 2026 |
| Pages: | 73--81 |
| DOI: | 10.1109/OJSP.2026.3652496 |
| Abstract: | Parallel to the development of advanced deepfake audio generation, audio deepfake detection has also seen significant progress. However, a standardized and comprehensive benchmark is still missing. To address this, we introduce Speech DeepFake (DF) Arena, the first comprehensive benchmark for audio deepfake detection. Speech DF Arena provides a toolkit to uniformly evaluate detection systems, currently across 14 diverse datasets and attack scenarios, standardized evaluation metrics and protocols for reproducibility and transparency. It also includes a leaderboard to compare and rank the systems to help researchers and developers enhance their reliability and robustness. We include 14 evaluation sets, 14 state-of-the-art open-source and 4 proprietary detection systems, totalling 18 systems in the leaderboard. Our study presents many systems exhibiting high EER in out-of-domain scenarios, highlighting the need for extensive cross-domain evaluation. The leaderboard is hosted on HuggingFace1 and a toolkit for reproducing results across the listed datasets is available on GitHub. |
| Main Research Program: | Sustainable & Resilient Societies |
| Keywords: | Anti-spoofing, audio deepfake, leaderboard |
| Projects: |
Idiap PaSS IICT |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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