DFGC 2022: The Second DeepFake Game Competition
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).
Code (1)
Tasks
BenchmarkingFace SwappingSimilar Papers 제목 키워드 기반
DFGC 2021: A DeepFake Game Competition
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…
BenchmarkingDeepFake DetectionFace SwappingDFGC-VRA: DeepFake Game Competition on Visual Realism Assessment
This paper presents the summary report on the DeepFake Game Competition on Visual Realism Assessment (DFGCVRA). Deep-learning based face-swap videos, also known as deepfakes, are becoming more and more realistic and dec…
Face SwappingBeyond Detection: Visual Realism Assessment of Deepfakes
In the era of rapid digitalization and artificial intelligence advancements, the development of DeepFake technology has posed significant security and privacy concerns. This paper presents an effective measure to assess …
Face SwappingVisual Realism Assessment for Face-swap Videos
Deep-learning based face-swap videos, also known as deep fakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. The research community has been …
DeepFake DetectionFace SwappingData-Driven Deepfake Image Detection Method -- The 2024 Global Deepfake Image Detection Challenge
With the rapid development of technology in the field of AI, deepfake technology has emerged as a double-edged sword. It has not only created a large amount of AI-generated content but also posed unprecedented challenges…
Data Augmentation