Hybrid Two-Stage Architecture for Tampering Detection of Chipless ID Cards

Abstract

Identity verification systems are widely used in daily life. Most of these systems rely on official documents containing identifying information about a person (i.e., passports, ID cards, driving licenses, amongst others). In this kind of approach, the identifiable data is contained inside the embedded chip in the ID card, and can be read remotely by an NFC-enabled mobile device and then matched with a frontal face photograph (selfie) of the person in question. Unfortunately, this method is limited in South-American countries, since only a few of them provide national ID cards that include embedded chips with the owner’s identifiable information. For instance, in countries such as Brazil—with a population of over 210 million people—the National ID card does not contain an embedded chip. This work explores a two-stage method, using deep learning techniques, to determine whether an ID card image provided remotely by the user is real, or tampered in the digital (composite) or non-digital domain (high-quality printed or digitally displayed on a screen). Furthermore, RGB images, frequency domain representation, noise features, and error level analysis are tested as different inputs to the two-stage classifier. The proposed BasicNet with Discrete Fourier Transform achieves the highest classification rates of 0.975 for real ID card images, and a mean of 0.968 for fake ID card images.