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@ARTICLE{Dowerah_IEEE_OJSP_2026,
                      author = {Dowerah, Sandipana and Kulkarni, Atharva and Kulkarni, Ajinkya and Tran, Hoan My and Kalda, Joonas and Fedorchenko, Artem and Fauve, Benoit and Lolive, Damien and alumae, Tanel and Magimai-Doss, Mathew},
                    keywords = {Anti-spoofing, audio deepfake, leaderboard},
                    projects = {Idiap, PaSS, IICT},
         mainresearchprogram = {Sustainable & Resilient Societies},
                       title = {Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models},
                     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.}
}