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

• URL: http://arxiv.org/abs/2001.03024v2
• Date: Fri, 11 Dec 2020 11:24:04 GMT
• Title: DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
• Authors: Liming Jiang, Ren Li, Wayne Wu, Chen Qian, Chen Change Loy
• Abstract summary: DeeperForensics-1.0 is the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames. 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.
• Score: 93.24684159708114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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