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Inclusion 2024 Global Multimedia Deepfake Detection Challenge: Towards Multi-dimensional Face Forgery Detection

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arxiv 2412.20833 v2 pith:7LGWWILV submitted 2024-12-30 cs.CV cs.MM

Yi Zhang , Weize Gao , Changtao Miao , Man Luo , Jianshu Li , Wenzhong Deng , Zhe Li , Bingyu Hu
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Weibin Yao Yunfeng Diao Wenbo Zhou Tao Gong Qi Chu
This is my paper · ORCID
classification cs.CVcs.MM
keywords detectionchallengedeepfaketeamsmultimediaaudio-videoforgeryglobal
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we present the Global Multimedia Deepfake Detection held concurrently with the Inclusion 2024. Our Multimedia Deepfake Detection aims to detect automatic image and audio-video manipulations including but not limited to editing, synthesis, generation, Photoshop,etc. Our challenge has attracted 1500 teams from all over the world, with about 5000 valid result submission counts. We invite the top 20 teams to present their solutions to the challenge, from which the top 3 teams are awarded prizes in the grand finale. In this paper, we present the solutions from the top 3 teams of the two tracks, to boost the research work in the field of image and audio-video forgery detection. The methodologies developed through the challenge will contribute to the development of next-generation deepfake detection systems and we encourage participants to open source their methods.

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  1. CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    CAD combines cross-modal lip-speech alignment with per-modality artifact distillation and reports 99.96% AUC on IDForge-v2, with strong cross-dataset results.

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