标题： 语音DF竞技场：语音深度伪造检测模型的排行榜 显示英文标题

标题： Speech DF Arena: A Leaderboard for Speech DeepFake Detection Models

摘要： 与先进深度伪造音频生成的发展并行，音频深度伪造检测也取得了显著进展。 然而，仍然缺乏标准化和全面的基准测试。 为了解决这个问题，我们引入了Speech DeepFake（DF）Arena，这是首个针对音频深度伪造检测的综合性基准测试。 Speech DF Arena提供了一个工具包，用于统一评估检测系统，目前涵盖14个多样化的数据集和攻击场景，以及标准化的评估指标和协议，以确保可重复性和透明度。 它还包括一个排行榜，用于比较和排名系统，以帮助研究人员和开发者提高其可靠性和鲁棒性。 我们包括14个评估集，12个最先进的开源系统和3个专有检测系统。 我们的研究显示，许多系统在跨领域场景中表现出较高的EER，突显了进行广泛跨领域评估的必要性。 排行榜托管在Huggingface1上，可在GitHub上获取用于在列出的数据集上重现结果的工具包。 显示英文摘要

摘要： 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, 12 state-of-the-art open-source and 3 proprietary detection systems. 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.