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.

翻译：生物特征系统在安全应用中的部署日益广泛，然而，这些系统仍易受欺骗攻击，攻击者利用伪造的生物特征数据获取未授权访问。本研究评估了MobileNetV2、DenseNet-121、Inception-v3和欺骗痕迹解缠（STD）等最先进机器学习模型在人脸识别系统中检测欺骗攻击的有效性。研究使用CelebA-Spoof数据集，通过准确率、精确率、召回率和F1分数等指标评估模型有效性，并采用MSU-MFSD数据集进行跨数据集验证以评估泛化能力。结果表明，MobileNetV2是最高效的模型，在平衡计算效能的同时达到92%的准确率，适用于实际应用场景。Inception-v3表现出中等鲁棒性，而DenseNet-121和STD在泛化方面存在困难。研究结果凸显了在领域自适应和混合架构方面取得进展以增强生物特征安全系统的必要性。