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

Liming Jiang, Ren Li, Wayne Wu, Chen Qian, Chen Change Loy; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 2889-2898

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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[bibtex]
@InProceedings{Jiang_2020_CVPR,
author = {Jiang, Liming and Li, Ren and Wu, Wayne and Qian, Chen and Loy, Chen Change},
title = {DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}