{"ID":2873906,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05592","arxiv_id":"2509.05592","title":"MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios","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}.","short_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 scenari...","url_abs":"https://arxiv.org/abs/2509.05592","url_pdf":"https://arxiv.org/pdf/2509.05592v1","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":"2025-09-06T04:36:41Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":610100,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2873906,"paper_url":"https://arxiv.org/abs/2509.05592","paper_title":"MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios","repo_url":"https://github.com/inclusionConf/MFFI","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}