Currently, it is ever more common to access online services for activities which formerly required physical attendance. From banking operations to visa applications, a significant number of processes have been digitised, especially since the advent of the COVID-19 pandemic, requiring remote biometric authentication of the user. On the downside, some subjects intend to interfere with the normal operation of remote systems for personal profit by using fake identity documents, such as passports and ID cards. Deep learning solutions to detect such frauds have been presented in the literature. However, due to privacy concerns and the sensitive nature of personal identity documents, developing a dataset with the necessary number of examples for training deep neural networks is challenging. This work explores three methods for synthetically generating ID card images to increase the amount of data while training fraud-detection networks. These methods include computer vision algorithms and Generative Adversarial Networks. Our results indicate that databases can be supplemented with synthetic images without any loss in performance for the print/scan Presentation Attack Instrument Species (PAIS) and a loss in performance of 1% for the screen capture PAIS.

中文翻译：

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用于改进演示攻击检测的合成 ID 卡图像生成


目前，对于以前需要亲自参加的活动，使用在线服务变得越来越普遍。从银行业务到签证申请，大量流程已经数字化，特别是自新冠肺炎 (COVID-19) 大流行出现以来，需要对用户进行远程生物识别身份验证。不利的一面是，一些主体意图通过使用伪造的身份证件（例如护照和身份证）来干扰远程系统的正常运行以获取个人利益。文献中已经提出了检测此类欺诈的深度学习解决方案。然而，由于隐私问题和个人身份证件的敏感性，开发具有必要数量示例的数据集来训练深度神经网络具有挑战性。这项工作探索了三种综合生成身份证图像的方法，以增加数据量，同时训练欺诈检测网络。这些方法包括计算机视觉算法和生成对抗网络。我们的结果表明，数据库可以补充合成图像，打印/扫描演示攻击工具种类 (PAIS) 的性能不会有任何损失，屏幕捕获 PAIS 的性能也不会损失 1%。