This paper evaluated the impact of synthetic images on Morphing Attack Detection (MAD) using a Siamese network with a semi-hard-loss function. Intra and cross-dataset evaluations were performed to measure synthetic image generalisation capabilities using a cross-dataset for evaluation. Three different pre-trained networks were used as feature extractors from traditional MobileNetV2, MobileNetV3 and EfficientNetB0. Our results show that MAD trained on EfficientNetB0 from FERET, FRGCv2, and FRLL can reach a lower error rate in comparison with SOTA. Conversely, worse performances were reached when the system was trained only with synthetic images. A mixed approach (synthetic + digital) database may help to improve MAD and reduce the error rate. This fact shows that we still need to keep going with our efforts to include synthetic images in the training process.

翻译：本文评估了使用半困难损失函数的孪生网络对合成图像在变形攻击检测（MAD）中的影响。通过跨数据集评估，测量了合成图像的泛化能力，并进行了数据集内与跨数据集两种评估。以传统MobileNetV2、MobileNetV3和EfficientNetB0为基础，采用三种不同的预训练网络作为特征提取器。结果表明，基于FERET、FRGCv2和FRLL数据集训练的EfficientNetB0的MAD系统，其误报率低于当前最优方法（SOTA）。相反，仅使用合成图像训练的系统性能较差。采用合成图像与数字图像混合的数据库有助于提升MAD性能并降低误报率。这表明仍需持续努力将合成图像纳入训练过程。