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 (\textbf{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}.
@article{arxiv.2509.05592,
title = {MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios},
author = {Changtao Miao and Yi Zhang and Man Luo and Weiwei Feng and Kaiyuan Zheng and Qi Chu and Tao Gong and Jianshu Li and Yunfeng Diao and Wei Zhou and Joey Tianyi Zhou and Xiaoshuai Hao},
journal= {arXiv preprint arXiv:2509.05592},
year = {2025}
}