# Synthetic Iris Presentation Attack using iDCGAN

arXiv: https://arxiv.org/abs/1710.10565 · HTML: https://mlsanity.com/paper/1710.10565/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2017.

## Built on by (most cited first, 17 of 17)

- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models](https://mlsanity.com/paper/2104.02107/) (2021)
- [Privacy-Safe Iris Presentation Attack Detection](https://mlsanity.com/paper/2408.02750/) (2024)
- [Forensic Iris Image Synthesis](https://mlsanity.com/paper/2312.04125/) (2023)
- [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)
- [iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris Images](https://mlsanity.com/paper/2305.12596/) (2023)
- [Reversing the Irreversible: A Survey on Inverse Biometrics](https://mlsanity.com/paper/2401.02861/) (2024)
- [Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review](https://mlsanity.com/paper/2205.10179/) (2022)
- [BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images](https://mlsanity.com/paper/2205.01536/) (2022)
- [A Multi-domain Image Translative Diffusion StyleGAN for Iris Presentation Attack Detection](https://mlsanity.com/paper/2510.14314/) (2025)
- [Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer](https://mlsanity.com/paper/2503.04707/) (2025)
- [Synthetic Iris Image Databases and Identity Leakage: Risks and Mitigation Strategies](https://mlsanity.com/paper/2506.02626/) (2025)
- [Compact Vision Models for Iris Presentation Attack Detection under Presentation Attack Instrument Shift and Environmental Degradation](https://mlsanity.com/paper/2609.20386/) (2026)
- [DFCANet: Dense Feature Calibration-Attention Guided Network for Cross Domain Iris Presentation Attack Detection](https://mlsanity.com/paper/2111.00919/) (2021)
- [Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations](https://mlsanity.com/paper/2511.09749/) (2025)

## Abstract (arXiv)

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.