  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.
