MobileNetV2在人脸欺骗检测中表现最佳，准确率达91.6%。
Face Spoofing Detection using Deep Learning
- 对比MobileNetV2、ResNet50和ViT三模型，选最优架构
- MobileNetV2测试集准确率91.59%，优于ViT的86.54%
- 模型轻量且泛化强，适合实际安全场景部署
数字图像伪造已成为依赖人脸识别的生物特征认证系统的重要安全威胁。本研究评估了MobileNetV2、ResNet50和视觉变压器（ViT）三种视觉模型在图像分类中的欺骗检测性能，使用包含150,986张图像的数据集，划分为训练集（140,002张）、测试集（10,984张）和验证集（39,574张）。通过准确率、精确率、召回率和F1分数对比模型效果。结果表明，MobileNetV2在测试集上表现最优，准确率91.59%、精确率91.72%、召回率91.59%、F1分数91.58%，显著优于ViT的86.54%、88.28%、86.54%、86.39%。在验证集上，MobileNetV2准确率97.17%，略高于ViT的96.36%。MobileNetV2训练收敛更快，对未见数据泛化能力更强，尽管两者均存在过拟合迹象。研究强调模型选择在安全敏感场景的重要性，推荐MobileNetV2作为实际部署的可行方案。
原文摘要 · Abstract (English)
Digital image spoofing has emerged as a significant security threat in biometric authentication systems, particularly those relying on facial recognition. This study evaluates the performance of three vision based models, MobileNetV2, ResNET50, and Vision Transformer, ViT, for spoof detection in image classification, utilizing a dataset of 150,986 images divided into training , 140,002, testing, 10,984, and validation ,39,574, sets. Spoof detection is critical for enhancing the security of image recognition systems, and this research compares the models effectiveness through accuracy, precision, recall, and F1 score metrics. Results reveal that MobileNetV2 outperforms other architectures on the test dataset, achieving an accuracy of 91.59%, precision of 91.72%, recall of 91.59%, and F1 score of 91.58%, compared to ViT 86.54%, 88.28%, 86.54%, and 86.39%, respectively. On the validation dataset, MobileNetV2, and ViT excel, with MobileNetV2 slightly ahead at 97.17% accuracy versus ViT 96.36%. MobileNetV2 demonstrates faster convergence during training and superior generalization to unseen data, despite both models showing signs of overfitting. These findings highlight MobileNetV2 balanced performance and robustness, making it the preferred choice for spoof detection applications where reliability on new data is essential. The study underscores the importance of model selection in security sensitive contexts and suggests MobileNetV2 as a practical solution for real world deployment.
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