Abstract
In this paper, an updated two-stage, end-to-end Presentation Attack Detection method for remote biometric verification systems of ID cards, based on MobileNetV2, is presented. Several presentation attack species such as printed, display, composite (based on cropped and spliced areas), plastic (PVC), and synthetic ID card images using different capture sources are used. This proposal was developed using a database consisting of 190.000 real case Chilean ID card images with the support of a third-party company. Also, a new framework called PyPAD, used to estimate multi-class metrics compliant with the ISO/IEC 30107-3 standard was developed, and will be made available for research purposes. Our method is trained on two convolutional neural networks separately, reaching BPCER\textsubscript{100} scores on ID cards attacks of 1.69\% and 2.36\% respectively. The two-stage method using both models together can reach a BPCER\textsubscript{100} score of 0.92\%.
Abstract (translated)
在本文中,我们介绍了基于MobileNetV2的移动身份验证系统ID卡远程身份验证方法的最新两阶段攻击检测方法。该方法使用多种Presentation攻击物种,如打印、显示、组合(基于裁剪和拼接区域)、塑料(PVC)和使用不同捕获来源生成的合成ID卡图像。该建议使用了一个由第三方公司支持的包含190,000个实际智利ID卡图像的数据库。此外,我们开发了名为PyPAD的新框架,用于估计符合ISO/IEC 30107-3标准的多分类指标,并将为研究提供。我们的方法分别通过两个卷积神经网络进行训练,在ID卡攻击方面分别实现了1.69%和2.36%的BPCER extsubscript{100}得分。同时,使用两个模型同时实现两阶段方法可以到达BPCER extsubscript{100}得分0.92%。
URL
https://arxiv.org/abs/2301.09542