ARTICLE Dowerah_IEEE_OJSP_2026/IDIAP Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models Dowerah, Sandipana Kulkarni, Atharva Kulkarni, Ajinkya Tran, Hoan My Kalda, Joonas Fedorchenko, Artem Fauve, Benoit Lolive, Damien alumae, Tanel Magimai-Doss, Mathew Anti-spoofing audio deepfake leaderboard IEEE Open Journal of Signal Processing 7 73--81 2026 10.1109/OJSP.2026.3652496 doi 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.