DFGC 2021: A DeepFake Game Competition
| Autor: | Wanyi Zhuang, Changtao Miao, Shan He, Hefei Ling, Yutong Yao, Wei Wang, Zhiliang Xu, Xiaoyan Wu, Baoying Chen, Guosheng Zhang, Yuezun Li, Han Chen, Shenghai Luo, Hongxing Fan, Qi Li, Boyuan Liu, Yanjie Hu, Zhenan Sun, Junrui Huang, Jing Dong, Changlei Lu, Bo Peng, Siwei Lyu |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Competition (economics)
FOS: Computer and information sciences Adversarial system Event (computing) Computer science Biometrics access control Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Benchmarking Computer security computer.software_genre computer |
| Zdroj: | IJCB |
| Popis: | This paper presents a summary of the DeepFake Game Competition (DFGC) 20211. 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 community2. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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