# Synthetic Iris Image Databases and Identity Leakage: Risks and Mitigation Strategies

arXiv: https://arxiv.org/abs/2506.02626 · HTML: https://mlsanity.com/paper/2506.02626/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 56 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 23 of 23)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [High-Fidelity Pluralistic Image Completion with Transformers](https://mlsanity.com/paper/2103.14031/) (2021)
- [DCFace: Synthetic Face Generation with Dual Condition Diffusion Model](https://mlsanity.com/paper/2304.07060/) (2023)
- [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)
- [Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition](https://mlsanity.com/paper/1807.05248/) (2018)
- [Synthetic Iris Presentation Attack using iDCGAN](https://mlsanity.com/paper/1710.10565/) (2017)
- [CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection](https://mlsanity.com/paper/2012.02374/) (2020)
- [Iris Recognition After Death](https://mlsanity.com/paper/1804.01962/) (2018)
- [Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN](https://mlsanity.com/paper/1812.04822/) (2018)
- [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)
- [A Parametric Approach to Adversarial Augmentation for Cross-Domain Iris Presentation Attack Detection](https://mlsanity.com/paper/2412.07199/) (2024)
- [EyePreserve: Identity-Preserving Iris Synthesis](https://mlsanity.com/paper/2312.12028/) (2023)
- [AugGen: Synthetic Augmentation using Diffusion Models Can Improve Recognition](https://mlsanity.com/paper/2503.11544/) (2025)
- [Iris Recognition for Infants](https://mlsanity.com/paper/2501.01375/) (2025)
- [Generating a Biometrically Unique and Realistic Iris Database](https://mlsanity.com/paper/2503.11930/) (2025)

## Built on by (most cited first, 1 of 1)

- [Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations](https://mlsanity.com/paper/2511.09749/) (2025)

## Abstract (arXiv)

This paper presents a comprehensive overview of iris image synthesis methods, which can alleviate the issues associated with gathering large, diverse datasets of biometric data from living individuals, which are considered pivotal for biometric methods development. These methods for synthesizing iris data range from traditional, hand crafted image processing-based techniques, through various iterations of GAN-based image generators, variational autoencoders (VAEs), as well as diffusion models. The potential and fidelity in iris image generation of each method is discussed and examples of inferred predictions are provided. Furthermore, the risks of individual biometric features leakage from the training sets are considered, together with possible strategies for preventing them, which have to be implemented should these generative methods be considered a valid replacement of real-world biometric datasets.