  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.
