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We present our on-going effort of constructing a large-scale benchmark forface forgery detection. The first version of this benchmark,DeeperForensics-1.0, represents the largest face forgery detection dataset byfar, with 60,000 videos constituted by a total of 17.6 million frames, 10 timeslarger than existing datasets of the same kind. Extensive real-worldperturbations are applied to obtain a more challenging benchmark of largerscale and higher diversity. All source videos in DeeperForensics-1.0 arecarefully collected, and fake videos are generated by a newly proposedend-to-end face swapping framework. The quality of generated videos outperformsthose in existing datasets, validated by user studies. The benchmark features ahidden test set, which contains manipulated videos achieving high deceptivescores in human evaluations. We further contribute a comprehensive study thatevaluates five representative detection baselines and make a thorough analysisof different settings.

DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection