# D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector

arXiv: https://arxiv.org/abs/2007.01381 · HTML: https://mlsanity.com/paper/2007.01381/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 26 of 26)

- [Overview frequency principle/spectral bias in deep learning](https://mlsanity.com/paper/2201.07395/) (2022)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Human-Aided Saliency Maps Improve Generalization of Deep Learning](https://mlsanity.com/paper/2105.03492/) (2021)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network](https://mlsanity.com/paper/2106.14845/) (2021)
- [Subspace Decomposition based DNN algorithm for elliptic type multi-scale PDEs](https://mlsanity.com/paper/2112.06660/) (2021)
- [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022)
- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)
- [Deep Learning for Iris Recognition: A Review](https://mlsanity.com/paper/2303.08514/) (2023)
- [A Parametric Approach to Adversarial Augmentation for Cross-Domain Iris Presentation Attack Detection](https://mlsanity.com/paper/2412.07199/) (2024)
- [Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances](https://mlsanity.com/paper/2111.12465/) (2021)
- [EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images](https://mlsanity.com/paper/2112.11610/) (2021)
- [Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack Detection](https://mlsanity.com/paper/2311.12764/) (2023)
- [Few-shot One-class Domain Adaptation Based on Frequency for Iris Presentation Attack Detection](https://mlsanity.com/paper/2204.00376/) (2022)
- [Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains](https://mlsanity.com/paper/2205.02573/) (2022)
- [A Multi-domain Image Translative Diffusion StyleGAN for Iris Presentation Attack Detection](https://mlsanity.com/paper/2510.14314/) (2025)
- [State Of The Art In Open-Set Iris Presentation Attack Detection](https://mlsanity.com/paper/2208.10564/) (2022)
- [Single Domain Dynamic Generalization for Iris Presentation Attack Detection](https://mlsanity.com/paper/2305.12800/) (2023)
- [SpectraIrisPAD: Leveraging Vision Foundation Models for Spectrally Conditioned Multispectral Iris Presentation Attack Detection](https://mlsanity.com/paper/2512.06103/) (2025)
- [DFCANet: Dense Feature Calibration-Attention Guided Network for Cross Domain Iris Presentation Attack Detection](https://mlsanity.com/paper/2111.00919/) (2021)
- [Iris Presentation Attack: Assessing the Impact of Combining Vanadium Dioxide Films with Artificial Eyes](https://mlsanity.com/paper/2311.12773/) (2023)
- [Training Better Deep Learning Models Using Human Saliency](https://mlsanity.com/paper/2410.16190/) (2024)
- [Task-conditioned Ensemble of Expert Models for Continuous Learning](https://mlsanity.com/paper/2504.08626/) (2025)
- [VISER: Visually-Informed System for Enhanced Robustness in Iris Presentation Attack Detection](https://mlsanity.com/paper/2603.17859/) (2026)
- [Beyond Mortality: Advancements in Post-Mortem Iris Recognition through Data Collection and Computer-Aided Forensic Examination](https://mlsanity.com/paper/2603.26976/) (2026)
- [Detecting Clear Contact Lenses for Iris Recognition: A Two-Stage Mask-Guided Attention Approach](https://mlsanity.com/paper/2608.08977/) (2026)

## Abstract (arXiv)

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.