Vision Transformer比传统CNN在人脸伪造检测中更公平，尤其对未见过的族裔群体表现更好。
Architectural Bias in Face Presentation Attack Detection: A Comparative Study of Vision Transformers and Convolutional Neural Networks
- 用ViT和CNN对比测试，发现预训练ViT泛化能力更强
- 在非洲与东亚人群间误检率差距缩小至0.13%，降低83%
- 对未知族裔群体的误报率仅为CNN的1/3.6，适合跨种族应用
人脸伪造攻击检测（PAD）是生物识别认证的关键安全层，但现有方法在不同族裔间存在系统性性能差异，尤其对深肤色个体不利。本文通过实证比较了视觉变压器（ViT）与卷积神经网络（CNN）在减少此类偏差方面的潜力。实验基于跨族裔人脸防伪数据集CeFA，评估了三种模型：从头训练的Multimodal ViT-Tiny、ResNet18基准模型，以及在CeFA上微调的预训练DeiT-S，覆盖非洲、东亚及零样本中央亚族裔群体。DeiT-S取得最高总体准确率97.27%和最低等错误率（EER）0.86%，优于ResNet18的90.15%。在公平性方面，德伊特-S将非洲与东亚受试者间的跨族裔ACER差距降至0.13%，较此前基于LBP的工作（0.75%）降低83%。最显著的是，当面对零样本中央亚族裔时，ResNet18的BPCER高达10.44%，而DeiT-S仅2.89%，展现出3.6倍的泛化优势。结果表明，预训练的视觉变压器在提高精度、缩小族裔性能差距和增强跨族裔泛化方面具有显著优势，提示架构设计可能影响跨族裔公平性。
原文摘要 · Abstract (English)
Face Presentation Attack Detection (PAD) systems constitute a critical security layer in biometric authentication; however, existing approaches exhibit systematic performance disparities across demographic groups, disproportionately affecting individuals with darker skin tones. This paper presents a comparative empirical investigation of whether Vision Transformer architectures reduce demographic bias in face PAD systems relative to convolutional baselines. Experiments are conducted on the CASIA-SURF Cross-Ethnicity Face Anti-Spoofing (CeFA) dataset. Three architectures are evaluated: a Multimodal ViT-Tiny trained from scratch, a ResNet18 CNN baseline, and a pretrained DeiT-S fine-tuned on CeFA across African, East Asian, and zero-shot Central Asian demographic groups. DeiT-S achieves the highest overall accuracy of 97.27% and the lowest EER of 0.86%, outperforming ResNet18 at 90.15% accuracy. In terms of fairness, DeiT-S reduces the inter-ethnic ACER gap between African and East Asian subjects to 0.13%, compared to 0.75% reported in an LBP-based work [6], representing an 83% reduction. Most notably, while ResNet18 records a BPCER of 10.44% on zero-shot Central Asian subjects, DeiT-S maintains 2.89% on the same unseen group, demonstrating a 3.6x generalization advantage. These results suggest that pretrained Vision Transformers achieve superior PAD accuracy, produce smaller demographic performance gaps, and generalize more equitably across unseen demographic groups, indicating that cross-demographic fairness in PAD may partly be influenced by architectural design.
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