DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results

This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection. The challenge employs the DeeperForensics-1.0 dataset, one of the most extensive publicly available real-world face forgery detection datasets, with 60,000 videos constituted by a total...

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Hauptverfasser:	Jiang, Liming, Guo, Zhengkui, Wu, Wayne, Liu, Zhaoyang, Liu, Ziwei, Loy, Chen Change, Yang, Shuo, Xiong, Yuanjun, Xia, Wei, Chen, Baoying, Zhuang, Peiyu, Li, Sili, Chen, Shen, Yao, Taiping, Ding, Shouhong, Li, Jilin, Huang, Feiyue, Cao, Liujuan, Ji, Rongrong, Lu, Changlei, Tan, Ganchao
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning
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Beschreibung
Zusammenfassung:	This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection. The challenge employs the DeeperForensics-1.0 dataset, one of the most extensive publicly available real-world face forgery detection datasets, with 60,000 videos constituted by a total of 17.6 million frames. The model evaluation is conducted online on a high-quality hidden test set with multiple sources and diverse distortions. A total of 115 participants registered for the competition, and 25 teams made valid submissions. We will summarize the winning solutions and present some discussions on potential research directions.
DOI:	10.48550/arxiv.2102.09471