This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the contrary, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform for benchmarking the game between the current state-of-the-arts in DeepFake creation and detection methods. The main research question to be answered by this competition is the current state of the two adversaries when competed with each other. This is the second edition after the last year's DFGC 2021, with a new, more diverse video dataset, a more realistic game setting, and more reasonable evaluation metrics. With this competition, we aim to stimulate research ideas for building better defenses against the DeepFake threats. We also release our DFGC 2022 dataset contributed by both our participants and ourselves to enrich the DeepFake data resources for the research community (https://github.com/NiCE-X/DFGC-2022).
翻译:本文介绍关于我们DFGC 2022竞争的简要报告。 DeepFake 正在迅速演变,现实的面部擦拭正在变得更具欺骗性,难以探测。 相反,发现深面镜的方法也在改善。深面镜创造者和捍卫者之间有两方的游戏。这个竞争为当前DFGC 2022 创建和检测方法中的最新艺术之间的游戏设定基准提供了一个共同的平台。这次竞争要回答的主要研究问题是双方对手在相互竞争时的现状。这是去年DFGC 2021 之后的第二版,有一个新的、更多样化的视频数据集、更现实的游戏设置和更合理的评估指标。有了这一竞争,我们的目标是激发研究理念,更好地防范深面镜威胁。我们还公布了我们的参与者和我们自己为丰富研究界的深面镜数据资源而提供的DFGC 2022 数据集(https://github.com/NiCE-X/DFGC-2022)。