发表机构
Pontifical Catholic University of Paraná（巴拉那天主教大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建基于MFFI数据集的人脸伪造检测基准，对比六类模型，发现干净集性能无法预测退化场景鲁棒性，DINOv3在退化集表现最优，凸显自监督表示的应用潜力。
AI 中文摘要
人脸伪造检测器在受控基准上通常能取得优异结果，但在真实图像退化场景下的可靠性仍有限。本文提出了一个标准化的人脸伪造检测基准，采用多维人脸伪造图像（Multi-Dimensional Face Forgery Image, MFFI）数据集，并在干净及退化的测试划分上评估性能。我们比较了六大模型家族，包括卷积网络、基于Transformer的模型以及冻结的自监督DINOv3骨干网络，涵盖空间、光谱及混合输入表示。结果显示，干净数据集上的性能并不能可靠指示模型在压缩、调整大小和模糊等退化场景下的鲁棒性。采用RGB输入的Xception在干净数据集上取得最佳性能，平均ROC-AUC达0.884，但在更困难的退化划分上性能大幅下降。相比之下，仅训练线性分类头的冻结DINOv3在退化数据集上取得最强结果，平均ROC-AUC为0.726。表示分析表明，傅里叶域线索与RGB信息结合时最有用，而纯光谱输入的表现始终逊于空间表示。定性归因图进一步显示，卷积检测器聚焦于局部伪造痕迹，而DINOv3则依赖更广泛的面部结构。这些发现强调了退化评估协议的必要性，并凸显自监督视觉表示是实现鲁棒人脸伪造检测的有前景方向。本文的源代码可在该httpsURL公开获取。
英文摘要
Face forgery detectors often achieve strong results on controlled benchmarks, but their reliability under realistic image degradations remains limited. This paper presents a standardized benchmark for face forgery detection using the Multi-Dimensional Face Forgery Image (MFFI) dataset and evaluates performance on both clean and degraded test partitions. We compare six model families, including convolutional networks, transformer-based models, and a frozen self-supervised DINOv3 backbone, across spatial, spectral, and hybrid input representations. The results show that clean-set performance is not a reliable indicator of robustness under compression, resizing, and blurring. Xception with RGB obtains the best clean performance, reaching 0.884 mean ROC-AUC, but degrades substantially on the harder partition. In contrast, frozen DINOv3 achieves the strongest degraded-set result, with 0.726 mean ROC-AUC, while training only a linear classification head. The representation analysis indicates that Fourier-domain cues are most useful when combined with RGB information, whereas purely spectral inputs consistently underperform spatial representations. Qualitative attribution maps further suggest that convolutional detectors focus on localized artifacts, while DINOv3 relies on broader facial structure. These findings reinforce the need for degraded evaluation protocols and highlight self-supervised visual representations as a promising direction for robust face forgery detection. Our source code is publicly available at https://github.com/lucasdocunha/FaceForgery-Benchmark/.
CommentsAccepted for presentation at the 2026 Conference on Graphics, Patterns and Images (SIBGRAPI)
