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

Autor:	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
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Technical report. Challenge website: https://competitions.codalab.org/competitions/25228
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.09471 Zobrazit plný text záznamu View this record from Arxiv