This paper investigates the use of synthetic face data to enhance Single-Morphing Attack Detection (S-MAD), addressing the limitations of availability of large-scale datasets of bona fide images due to privacy concerns. Various morphing tools and cross-dataset evaluation schemes were utilized to conduct this study. An incremental testing protocol was implemented to assess the generalization capabilities as more and more synthetic images were added. The results of the experiments show that generalization can be improved by carefully incorporating a controlled number of synthetic images into existing datasets or by gradually adding bona fide images during training. However, indiscriminate use of synthetic data can lead to sub-optimal performance. Evenmore, the use of only synthetic data (morphed and non-morphed images) achieves the highest Equal Error Rate (EER), which means in operational scenarios the best option is not relying only on synthetic data for S-MAD.

翻译：本文研究了利用合成人脸数据增强单次变形攻击检测（S-MAD）的方法，以解决因隐私问题导致的真实图像大规模数据集可用性受限的挑战。本研究采用了多种变形工具和跨数据集评估方案，并实施了增量测试协议来评估随着合成图像逐步增加时的泛化能力。实验结果表明，通过将受控数量的合成图像谨慎整合到现有数据集中，或在训练过程中逐步添加真实图像，可以提升模型的泛化性能。然而，不加选择地使用合成数据可能导致次优结果。此外，仅使用合成数据（变形与非变形图像）会得到最高的等错误率（EER），这意味着在实际应用场景中，最佳选择并非完全依赖合成数据进行S-MAD。