  An iris recognition system is vulnerable to presentation attacks, or PAs,
where an adversary presents artifacts such as printed eyes, plastic eyes, or
cosmetic contact lenses to circumvent the system. In this work, we propose an
effective and robust iris PA detector called D-NetPAD based on the DenseNet
convolutional neural network architecture. It demonstrates generalizability
across PA artifacts, sensors and datasets. Experiments conducted on a
proprietary dataset and a publicly available dataset (LivDet-2017) substantiate
the effectiveness of the proposed method for iris PA detection. The proposed
method results in a true detection rate of 98.58\% at a false detection rate of
0.2\% on the proprietary dataset and outperfoms state-of-the-art methods on the
LivDet-2017 dataset. We visualize intermediate feature distributions and
fixation heatmaps using t-SNE plots and Grad-CAM, respectively, in order to
explain the performance of D-NetPAD. Further, we conduct a frequency analysis
to explain the nature of features being extracted by the network. The source
code and trained model are available at https://github.com/iPRoBe-lab/D-NetPAD.
