构建多维度真实场景伪造人脸数据集，提升检测模型泛化能力。
MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios
- 涵盖50种伪造方法，覆盖多种真实场景与真实数据来源。
- 包含102.4万张图像，具备复杂场景与多级退化特性。
- 适合用于训练和评估真实世界中抗干扰的深度伪造检测模型。
人工智能生成内容（AIGC）的快速发展使人脸伪造技术日益逼真，对社会安全构成严重威胁。然而，现有深度伪造检测方法受限于数据集多样性不足，难以模拟真实场景。具体表现在：未知的先进伪造技术、面部场景变化、真实数据丰富度不足以及真实传播中的退化问题。为此，我们提出多维度人脸伪造图像数据集（MFFI），专为真实场景设计。MFFI从四个维度增强真实性：1）更广泛的伪造方法；2）多样化的面部场景；3）多样化的真实数据；4）多层次退化操作。MFFI集成50种不同伪造方法，共包含1024K张图像样本。基准测试表明，MFFI在场景复杂度、跨域泛化能力和检测难度梯度方面均优于现有公开数据集，验证了其在模拟真实条件下的技术先进性与实用价值。数据集及相关信息已公开于https://github.com/inclusionConf/MFFI。
原文摘要 · Abstract (English)
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}.
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