# Alpha-wolves and Alpha-mammals: Exploring Dictionary Attacks on Iris Recognition Systems

arXiv: https://arxiv.org/abs/2403.12047 · HTML: https://mlsanity.com/paper/2403.12047/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution](https://mlsanity.com/paper/2108.01077/) (2021)
- [On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations](https://mlsanity.com/paper/2203.12387/) (2022)
- [Dictionary Attacks on Speaker Verification](https://mlsanity.com/paper/2204.11304/) (2022)

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

- [Generating a Biometrically Unique and Realistic Iris Database](https://mlsanity.com/paper/2503.11930/) (2025)

## Abstract (arXiv)

A dictionary attack in a biometric system entails the use of a small number of strategically generated images or templates to successfully match with a large number of identities, thereby compromising security. We focus on dictionary attacks at the template level, specifically the IrisCodes used in iris recognition systems. We present an hitherto unknown vulnerability wherein we mix IrisCodes using simple bitwise operators to generate alpha-mixtures - alpha-wolves (combining a set of "wolf" samples) and alpha-mammals (combining a set of users selected via search optimization) that increase false matches. We evaluate this vulnerability using the IITD, CASIA-IrisV4-Thousand and Synthetic datasets, and observe that an alpha-wolf (from two wolves) can match upto 71 identities @FMR=0.001%, while an alpha-mammal (from two identities) can match upto 133 other identities @FMR=0.01% on the IITD dataset.