  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\%.
