对比四种模型在人脸欺骗检测中的表现，发现MobileNetV2最适合作实时应用。
On the Study of Biometric Spoofing Detection using Deep Learning
- 用MobileNetV2等四模型在CelebA-Spoof上检测人脸伪造
- MobileNetV2达92%准确率，计算效率最优
- 强调需改进跨数据集泛化能力，适合安全系统研发者
生物识别系统广泛应用于安全场景，但仍易受伪造攻击。本研究评估了MobileNetV2、DenseNet-121、Inception-v3及Spoof Trace Disentanglement（STD）四种先进机器学习模型在人脸识别系统中检测欺骗攻击的效果。基于CelebA-Spoof数据集，采用准确率、精确率、召回率和F1分数进行评估。同时在MSU-MFSD数据集上进行跨数据集验证，以测试模型泛化能力。结果表明，MobileNetV2在保持92%准确率的同时具备最佳计算效率，适用于实际部署；Inception-v3表现中等，而DenseNet-121与STD在泛化方面表现不佳。研究指出需推动领域自适应与混合架构发展，以提升生物识别系统的安全性。
原文摘要 · Abstract (English)
Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access. This research evaluates the effectiveness of state-of-the-art machine learning models, MobileNetV2, DenseNet-121, Inception-v3, and Spoof Trace Disentanglement (STD) in detecting spoofing attacks within facial recognition systems. Using the CelebA-Spoof dataset, the study evaluates model effectiveness using metrics such as accuracy, precision, recall, and F1 Score. Cross-dataset validation is carried out on the MSU-MFSD dataset to assess generalizability. The results show MobileNetV2 as the most efficient model, achieving 92% accuracy while balancing computational effectiveness, making it appropriate for real-life applications. Inception-v3 shows moderate robustness, while DenseNet-121 and STD struggle with generalization. The findings highlight the need for advances in domain adaptation and hybrid architectures to enhance biometric security systems.
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