The accurate detection of ID card Presentation Attacks (PA) is becoming increasingly important due to the rising number of online/remote services that require the presentation of digital photographs of ID cards for digital onboarding or authentication. Furthermore, cybercriminals are continuously searching for innovative ways to fool authentication systems to gain unauthorized access to these services. Although advances in neural network design and training have pushed image classification to the state of the art, one of the main challenges faced by the development of fraud detection systems is the curation of representative datasets for training and evaluation. The handcrafted creation of representative presentation attack samples often requires expertise and is very time-consuming, thus an automatic process of obtaining high-quality data is highly desirable. This work explores ID card Presentation Attack Instruments (PAI) in order to improve the generation of samples with four Generative Adversarial Networks (GANs) based image translation models and analyses the effectiveness of the generated data for training fraud detection systems. Using open-source data, we show that synthetic attack presentations are an adequate complement for additional real attack presentations, where we obtain an EER performance increase of 0.63% points for print attacks and a loss of 0.29% for screen capture attacks.

翻译：身份证呈现攻击（PA）的准确检测正变得日益重要，这是因为越来越多的在线/远程服务要求在数字注册或身份认证过程中出示身份证的数字照片。此外，网络犯罪分子不断寻找创新手段来欺骗认证系统，以非法获取对这些服务的访问权限。尽管神经网络设计与训练的进步已将图像分类推至当前最佳水平，但欺诈检测系统开发面临的主要挑战之一，仍是用于训练与评估的代表性数据集的整理。手工制作代表性的呈现攻击样本通常需要专业知识且非常耗时，因此自动获得高质量数据的方法极具价值。本文探索了身份证呈现攻击手段（PAI），通过四种基于生成对抗网络（GAN）的图像翻译模型改进样本生成，并分析了所生成数据用于训练欺诈检测系统的有效性。利用开源数据，我们证明合成攻击呈现是额外真实攻击呈现的恰当补充，其中打印攻击的等错误率（EER）性能提升0.63个百分点，屏幕截取攻击的性能损失为0.29个百分点。