In this paper, we present our on-going effort of constructing a large-scale benchmark, DeeperForensics-1.0, for face forgery detection. Our benchmark represents the largest face forgery detection dataset by far, with 60, 000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind. Extensive real-world perturbations are applied to obtain a more challenging benchmark of larger scale and higher diversity. All source videos in DeeperForensics-1.0 are carefully collected, and fake videos are generated by a newly proposed end-to-end face swapping framework. The quality of generated videos outperforms those in existing datasets, validated by user studies. The benchmark features a hidden test set, which contains manipulated videos achieving high deceptive scores in human evaluations. We further contribute a comprehensive study that evaluates five representative detection baselines and make a thorough analysis of different settings. We believe this dataset will contribute to real-world face forgery detection research.

本文介绍了一个大规模人脸伪造检测基准DeeperForensics-1.0，由新提出的端到端人脸交换框架生成。它包含60,000个视频和17.6万帧，是同类现有数据集的十倍大。该基准集有一个隐藏的测试集，包含了通过对真实视频进行广泛应用的方法制造的高欺骗得分的操作视频，并评估了五个代表性的检测基线并进行了彻底分析。

DeeperForensics-1.0: 用于现实世界人脸伪造检测的大规模数据集