Online Augmentation for Presentation Attack Detection on ID Cards

This work proposes a Presentation of Attack Detection on ID Card systems, focusing on improving the detection of manual composite attacks. These kinds of attacks are used in a remote verification system to impersonate the identity of one subject in order to obtain benefits. For this purpose, relevant text can be modified, copied, and pasted. Faces and other areas can be manually changed because of the alignment of the originally issued ID card to a fake ID card. This work proposed an “online” methodology to create composite attack images during the training process and shows that using automatic composite attacks may reduce the error rate in the PAD system. To demonstrate it, three experiments were set up that compared the performance of models trained with and without the aforementioned technique on a private dataset and a cross-evaluation dataset.