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Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection

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arxiv 2207.13505 v1 pith:XJ74ZPHZ submitted 2022-07-27 cs.CV

Jianshu Li , Man Luo , Jian Liu , Tao Chen , Chengjie Wang , Ziwei Liu , Shuo Liu , Kewei Yang
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Xuning Shao Kang Chen Boyuan Liu Mingyu Guo Ying Guo Yingying Ao Pengfei Gao
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classification cs.CV
keywords imagechallengedetectionteamsmulti-forgeryforgerysolutionsaims
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In this paper, we present the Multi-Forgery Detection Challenge held concurrently with the IEEE Computer Society Workshop on Biometrics at CVPR 2022. Our Multi-Forgery Detection Challenge aims to detect automatic image manipulations including but not limited to image editing, image synthesis, image generation, image photoshop, etc. Our challenge has attracted 674 teams from all over the world, with about 2000 valid result submission counts. We invited the Top 10 teams to present their solutions to the challenge, from which three teams are awarded prizes in the grand finale. In this paper, we present the solutions from the Top 3 teams, in order to boost the research work in the field of image forgery detection.

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  1. MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios

    cs.CV 2025-09 conditional novelty 6.0 of 10

    MFFI is a 1,024,000-image face forgery benchmark spanning 50 (listed 51) forgery techniques, varied facial scenes, multiple real-data sources, and multi-level degradation operations.

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