Identifying fraudulent identity documents by analyzing imprinted guilloche patterns

Identity document (ID) verification is crucial in fostering trust in the digital realm, especially with the increasing shift of transactions to online platforms. Our research, building upon our previous work (Al-Ghadi et al. 2023), delves deeper into ID verification by focusing on guilloche patterns. We present two innovative ID verification models leveraging contrastive and adversarial learning. These models enhance guilloche pattern detection, offering new insights into identifying counterfeit IDs. Each approach comprises two main components: (i) guilloche pattern recognition and feature generation using a convolutional neural network (CNN), and (ii) precise classification of input data as authentic or forged. We evaluate our models extensively on the MIDV and FMIDV datasets, achieving accuracy and F1-score results ranging from 68-92% and 75-100%, respectively. Our study, incorporating contrastive and adversarial learning, contributes significantly to the ongoing discourse on ID verification, specifically in analyzing guilloche patterns.