AV-Deepfake1M++：一个带有真实世界扰动的大规模音视频深度伪造基准
AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations
AI总结：
针对深度伪造检测缺乏含真实扰动的大规模数据集的问题，研究团队提出含200万片段、带多样化篡改与音视频扰动的基准AV-Deepfake1M++，完成基准测试并基于其主办2025年1M-Deepfakes检测挑战赛，助力深度伪造领域研究。
摘要：
文本转语音与人脸-声音重演模型的快速涌现使得视频伪造变得更易实现且逼真度极高。为应对这一问题，我们需要具备丰富生成方法类型、且包含在线视频中常见扰动策略的数据集。为此，我们提出AV-Deepfake1M++，它是AV-Deepfake1M的扩展版本，包含200万个视频片段，具备多样化的篡改策略与音视频扰动。本文介绍了数据生成策略，并使用当前最优方法在AV-Deepfake1M++上开展了基准测试。我们认为该数据集将为推动深度伪造领域的研究发挥关键作用。基于该数据集，我们主办了2025年1M-Deepfakes检测挑战赛，挑战赛详情、数据集与评估脚本已通过仅供研究使用的许可在https://deepfakes1m.github.io/2025线上公开。
英文摘要：
The rapid surge of text-to-speech and face-voice reenactment models makes video fabrication easier and highly realistic. To encounter this problem, we require datasets that rich in type of generation methods and perturbation strategy which is usually common for online videos. To this end, we propose AV-Deepfake1M++, an extension of the AV-Deepfake1M having 2 million video clips with diversified manipulation strategy and audio-visual perturbation. This paper includes the description of data generation strategies along with benchmarking of AV-Deepfake1M++ using state-of-the-art methods. We believe that this dataset will play a pivotal role in facilitating research in Deepfake domain. Based on this dataset, we host the 2025 1M-Deepfakes Detection Challenge. The challenge details, dataset and evaluation scripts are available online under a research-only license at https://deepfakes1m.github.io/2025.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
