DFGC 2021: A DeepFake Game Competition
원문 초록
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.
함께 살펴볼 논문
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Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes
Unmasking real-world audio deepfakes: A data-centric approach
TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
토론
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