# Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances

arXiv: https://arxiv.org/abs/2111.12465 · HTML: https://mlsanity.com/paper/2111.12465/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 75 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation](https://mlsanity.com/paper/1901.01708/) (2019)
- [Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition](https://mlsanity.com/paper/2009.00749/) (2020)
- [D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector](https://mlsanity.com/paper/2007.01381/) (2020)
- [Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art](https://mlsanity.com/paper/1804.00194/) (2018)
- [Deep Representations for Iris, Face, and Fingerprint Spoofing Detection](https://mlsanity.com/paper/1410.1980/) (2014)
- [CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection](https://mlsanity.com/paper/2012.02374/) (2020)
- [Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network](https://mlsanity.com/paper/2106.14845/) (2021)
- [Presentation Attack Detection for Cadaver Iris](https://mlsanity.com/paper/1807.04058/) (2018)
- [Robust Iris Presentation Attack Detection Fusing 2D and 3D Information](https://mlsanity.com/paper/2002.09137/) (2020)
- [Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study](https://mlsanity.com/paper/2004.12040/) (2020)
- [Iris Presentation Attack Detection Based on Photometric Stereo Features](https://mlsanity.com/paper/1811.07252/) (2018)
- [Direct attacks using fake images in iris verification](https://mlsanity.com/paper/2111.00178/) (2021)
- [Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems](https://mlsanity.com/paper/1609.01461/) (2016)

## Built on by (most cited first, 3 of 3)

- [Periocular Biometrics: A Modality for Unconstrained Scenarios](https://mlsanity.com/paper/2212.13792/) (2022)
- [Toward Face Biometric De-identification using Adversarial Examples](https://mlsanity.com/paper/2302.03657/) (2023)
- [A Systematic Failure Analysis of Vision Foundation Models for Open Set Iris Presentation Attack Detection](https://mlsanity.com/paper/2605.19020/) (2026)

## Abstract (arXiv)

Iris recognition technology has attracted an increasing interest in the last decades in which we have witnessed a migration from research laboratories to real world applications. The deployment of this technology raises questions about the main vulnerabilities and security threats related to these systems. Among these threats presentation attacks stand out as some of the most relevant and studied. Presentation attacks can be defined as presentation of human characteristics or artifacts directly to the capture device of a biometric system trying to interfere its normal operation. In the case of the iris, these attacks include the use of real irises as well as artifacts with different level of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks. First, we summarise the most popular types of attacks including the main challenges to address. Secondly, we present a taxonomy of Presentation Attack Detection methods as a brief introduction to this very active research area. Finally, we discuss the integration of these methods into Iris Recognition Systems according to the most important scenarios of practical application.