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DeeperForensics-1.0

A Large-Scale Dataset for Real-World Face Forgery Detection

DeeperForensics-1.0

In this paper, we present our on-going effort of constructing a large-scale benchmark, DeeperForensics-1.0, for face forgery detection. Our benchmark 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. We believe this dataset will contribute to real-world face forgery detection research.

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Nanyang Technological University
https://www.ntu.edu.sg
Task
GAN / Image Generation
Annotation Types
Keypoint Skeleton
17600000
Items
35
Classes
17600000
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
Research Only
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