MFFI: Multi-dimensional Face Forgery Image dataset for real-world scenarios

Miao, Changtao, Zhang, Yi, Luo, Man, Feng, Weiwei, Zheng, Kaiyuan, Chu, Qi, Gong, Tao, Li, Jianshu, Diao, Yunfeng, Zhou, Wei, Zhou, Joey Tianyi and Hao, Xiaoshuai 2025. MFFI: Multi-dimensional Face Forgery Image dataset for real-world scenarios. Presented at: MM '25: The 33rd ACM International Conference on Multimedia, Dublin, Ireland, 27-31 October 2025. MM '25: Proceedings of the 33rd ACM International Conference on Multimedia. ACM, pp. 13235-13242. 10.1145/3746027.3758280

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Abstract

Rapid advances in Artificial Intelligence Generated Content (AIGC) have enabled increasingly sophisticated face forgeries, posing a significant threat to social security. However, current Deepfake detection methods are limited by constraints in existing datasets, which lack the diversity necessary in real-world scenarios. Specifically, these data sets fall short in four key areas: unknown of advanced forgery techniques, variability of facial scenes, richness of real data, and degradation of real-world propagation. To address these challenges, we propose the Multi-dimensional Face Forgery Image (MFFI ) dataset, tailored for real-world scenarios. MFFI enhances realism based on four strategic dimensions: 1) Wider Forgery Methods; 2) Varied Facial Scenes; 3) Diversified Authentic Data; 4) Multi-level Degradation Operations. MFFI integrates 50 different forgery methods and contains 1024K image samples. Benchmark evaluations show that MFFI outperforms existing public datasets in terms of scene complexity, cross-domain generalization capability, and detection difficulty gradients. These results validate the technical advance and practical utility of MFFI in simulating real-world conditions. The dataset and additional details are publicly available at https://github.com/inclusionConf/MFFI.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	ACM
ISBN:	9798400720352
Date of First Compliant Deposit:	18 November 2025
Last Modified:	18 Nov 2025 10:33
URI:	https://orca.cardiff.ac.uk/id/eprint/182478

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