# Privacy-Safe Iris Presentation Attack Detection

arXiv: https://arxiv.org/abs/2408.02750 · HTML: https://mlsanity.com/paper/2408.02750/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Training Generative Adversarial Networks with Limited Data](https://mlsanity.com/paper/2006.06676/) (2020)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models](https://mlsanity.com/paper/2101.05084/) (2020)
- [Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation](https://mlsanity.com/paper/1901.01708/) (2019)
- [Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art](https://mlsanity.com/paper/1804.00194/) (2018)
- [Synthetic Iris Presentation Attack using iDCGAN](https://mlsanity.com/paper/1710.10565/) (2017)
- [SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data](https://mlsanity.com/paper/2303.02660/) (2023)
- [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: Where Are We Now?](https://mlsanity.com/paper/2006.13252/) (2020)
- [Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition](https://mlsanity.com/paper/2310.04541/) (2023)
- [Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN](https://mlsanity.com/paper/1812.04822/) (2018)
- [Robust Iris Presentation Attack Detection Fusing 2D and 3D Information](https://mlsanity.com/paper/2002.09137/) (2020)
- [Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises](https://mlsanity.com/paper/2211.05629/) (2022)
- [SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data](https://mlsanity.com/paper/2311.05336/) (2023)
- [Forensic Iris Image Synthesis](https://mlsanity.com/paper/2312.04125/) (2023)
- [DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation](https://mlsanity.com/paper/2207.08980/) (2022)
- [iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris Images](https://mlsanity.com/paper/2305.12596/) (2023)
- [EyePreserve: Identity-Preserving Iris Synthesis](https://mlsanity.com/paper/2312.12028/) (2023)

## Built on by (most cited first, 6 of 6)

- [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)
- [LitMAS: A Lightweight and Generalized Multi-Modal Anti-Spoofing Framework for Biometric Security](https://mlsanity.com/paper/2506.06759/) (2025)
- [Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations](https://mlsanity.com/paper/2511.09749/) (2025)
- [Mitigating Longitudinal Performance Degradation in Child Face Recognition Using Synthetic Data](https://mlsanity.com/paper/2601.01689/) (2026)
- [A Systematic Failure Analysis of Vision Foundation Models for Open Set Iris Presentation Attack Detection](https://mlsanity.com/paper/2605.19020/) (2026)

## Abstract (arXiv)

This paper proposes a framework for a privacy-safe iris presentation attack detection (PAD) method, designed solely with synthetically-generated, identity-leakage-free iris images. Once trained, the method is evaluated in a classical way using state-of-the-art iris PAD benchmarks. We designed two generative models for the synthesis of ISO/IEC 19794-6-compliant iris images. The first model synthesizes bona fide-looking samples. To avoid ``identity leakage,'' the generated samples that accidentally matched those used in the model's training were excluded. The second model synthesizes images of irises with textured contact lenses and is conditioned by a given contact lens brand to have better control over textured contact lens appearance when forming the training set. Our experiments demonstrate that models trained solely on synthetic data achieve a lower but still reasonable performance when compared to solutions trained with iris images collected from human subjects. This is the first-of-its-kind attempt to use solely synthetic data to train a fully-functional iris PAD solution, and despite the performance gap between regular and the proposed methods, this study demonstrates that with the increasing fidelity of generative models, creating such privacy-safe iris PAD methods may be possible. The source codes and generative models trained for this work are offered along with the paper.