Large-Scale Chinese Data Benchmark for Face Video Anti-Forgery Identification

Subjects: Computer Science >> Information Security submitted time 2024-01-22

BEI Yi-Jun LOU Heng-Rui GAO Ke-Wei SONG Jie WANG Rui JIN Cang-Hong SONG Ming-Li FENG Zun-Lei

Abstract： With the rapid development of AIGC (Artificial Intelligence Generated Content) technology, hyper-realistic forged facial videos have become capable of deceiving human visual perception. As a result, a significant number of facial anti-forgery detection algorithms have been proposed for the identification of these fake facial videos. However, effectively evaluating the efficacy and applicability of these forgery detection algorithms remains a substantial challenge. To effectively promote the quantitative assessment of facial anti-forgery detection performance and the iterative development of anti-forgery technologies, this paper introduces a large-scale Chinese data benchmark for facial video anti-forgery identification and releases the world's first CHN-DF Chinese dataset (https://github.com/HengruiLou/CHN-DF), filling the gap in facial video anti-forgery datasets in terms of large-scale Chinese data. The paper details the process of constructing the CHN-DF dataset and the Chinese data evaluation benchmark and validates the complexity and realism of the CHN-DF dataset through experiments. It is hoped that this evaluation benchmark will assist researchers in building more practical and effective facial video anti-forgery detection models, thereby advancing the technology in the field of anti-forgery detection. Additionally, this paper addresses the challenges posed by Chinese face video anti- forgery detection benchmark datasets and anti-forgery detection technology. It also proposes potential future research directions, offering valuable insights to advance the development of face video anti-forgery detection technology.
 

Peer Review Status:Awaiting Review

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