We present our on-going effort of constructing a large-scale benchmark for face forgery detection. The first version of this benchmark, DeeperForensics-1.0, 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.
@article{arxiv.2001.03024,
title = {DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection},
author = {Liming Jiang and Ren Li and Wayne Wu and Chen Qian and Chen Change Loy},
journal= {arXiv preprint arXiv:2001.03024},
year = {2020}
}