Synthetic Iris Presentation Attack using iDCGAN
This work addresses security concerns for critical applications like border control by revealing a novel attack method that could compromise iris biometric systems.
The authors tackled the vulnerability of iris recognition systems by generating synthetic iris images using a deep learning framework called iDCGAN, and demonstrated that these images can successfully fool a commercial iris recognition system, with the state-of-the-art DESIST detection framework failing to discriminate them from real images.
Reliability and accuracy of iris biometric modality has prompted its large-scale deployment for critical applications such as border control and national ID projects. The extensive growth of iris recognition systems has raised apprehensions about susceptibility of these systems to various attacks. In the past, researchers have examined the impact of various iris presentation attacks such as textured contact lenses and print attacks. In this research, we present a novel presentation attack using deep learning based synthetic iris generation. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, we propose a new framework, named as iDCGAN (iris deep convolutional generative adversarial network) for generating realistic appearing synthetic iris images. We demonstrate the effect of these synthetically generated iris images as presentation attack on iris recognition by using a commercial system. The state-of-the-art presentation attack detection framework, DESIST is utilized to analyze if it can discriminate these synthetically generated iris images from real images. The experimental results illustrate that mitigating the proposed synthetic presentation attack is of paramount importance.