Abstract

This paper introduces a new hybrid two-stage multiclass network for end-to-end Presentation Attack Detection in remote biometric verification systems using ID cards based on MobileNetV2. Using different capture sources, several presentation attacks species such as printing, display, composite (based on cropped and spliced areas), plastic (PVC), and synthetic ID card images. 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 BPCER100 scores on ID card attacks of 1.69% for network 1 and 2.36% for network 2. Using both models, The two-stage concatenated method can achieve a BPCER100 score of 0.92%. © 2025 The Author(s)