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

Authors: Changtao Miao, Yi Zhang, Man Luo, Weiwei Feng, Kaiyuan Zheng, Qi Chu, Tao Gong, Jianshu Li, Yunfeng Diao, Wei Zhou, Joey Tianyi Zhou, Xiaoshuai Hao

Published: 2025-09-06 04:36:41+00:00

AI Summary

The paper introduces the MFFI dataset, a large-scale and diverse dataset for real-world face forgery detection. MFFI addresses limitations of existing datasets by incorporating various forgery techniques, diverse facial scenes, authentic data sources, and real-world degradation operations.

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 (\\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}.