DFGC 2021: A DeepFake Game Competition
Bo Peng, Hongxing Fan, Wei Wang, Jing Dong, Yuezun Li, Siwei Lyu, Qi, Li, Zhenan Sun, Han Chen, Baoying Chen, Yanjie Hu, Shenghai Luo, Junrui, Huang, Yutong Yao, Boyuan Liu, Hefei Ling, Guosheng Zhang, Zhiliang Xu,, Changtao Miao, Changlei Lu, Shan He, Xiaoyan Wu, Wanyi Zhuang
TL;DR
The DFGC 2021 competition benchmarked the latest DeepFake creation and detection methods, highlighting advancements and challenges in the adversarial game between DeepFake generators and detectors, and providing a dataset for future research.
Contribution
This paper summarizes the organization, results, top solutions, and insights from the DFGC 2021 DeepFake game competition, and releases a new testing dataset for research.
Findings
Top detection methods achieved high accuracy in identifying DeepFakes.
DeepFake generation techniques continue to improve, challenging detection methods.
The competition fostered collaboration and benchmarking in DeepFake technology.
Abstract
This paper presents a summary of the DFGC 2021 competition. DeepFake technology is developing fast, and realistic face-swaps are increasingly deceiving and hard to detect. At the same time, DeepFake detection methods are also improving. There is a two-party game between DeepFake creators and detectors. This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods. In this paper, we present the organization, results and top solutions of this competition and also share our insights obtained during this event. We also release the DFGC-21 testing dataset collected from our participants to further benefit the research community.
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