Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection
Jianshu Li, Man Luo, Jian Liu, Tao Chen, Chengjie Wang, Ziwei Liu,, Shuo Liu, Kewei Yang, Xuning Shao, Kang Chen, Boyuan Liu, Mingyu Guo, Ying, Guo, Yingying Ao, Pengfei Gao
TL;DR
This paper introduces the Multi-Forgery Detection Challenge 2022, which aims to advance the detection of diverse image forgeries through a large-scale competition involving global teams and top solutions.
Contribution
It presents a large-scale challenge with diverse forgery detection tasks, showcasing top solutions to push research in automatic image manipulation detection.
Findings
High participation with 674 teams and 2000 submissions
Top solutions demonstrate effective detection of various image forgeries
The challenge fosters progress in unconstrained forgery detection methods
Abstract
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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Taxonomy
TopicsDigital Media Forensic Detection · Adversarial Robustness in Machine Learning · Advanced Neural Network Applications