# Analysis of Master Vein Attacks on Finger Vein Recognition Systems

arXiv: https://arxiv.org/abs/2210.10667 · HTML: https://mlsanity.com/paper/2210.10667/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 18 Oct 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems](https://mlsanity.com/paper/2006.08376/) (2020)
- [Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution](https://mlsanity.com/paper/2108.01077/) (2021)
- [Master Face Attacks on Face Recognition Systems](https://mlsanity.com/paper/2109.03398/) (2021)

## Abstract (arXiv)

Finger vein recognition (FVR) systems have been commercially used, especially in ATMs, for customer verification. Thus, it is essential to measure their robustness against various attack methods, especially when a hand-crafted FVR system is used without any countermeasure methods. In this paper, we are the first in the literature to introduce master vein attacks in which we craft a vein-looking image so that it can falsely match with as many identities as possible by the FVR systems. We present two methods for generating master veins for use in attacking these systems. The first uses an adaptation of the latent variable evolution algorithm with a proposed generative model (a multi-stage combination of beta-VAE and WGAN-GP models). The second uses an adversarial machine learning attack method to attack a strong surrogate CNN-based recognition system. The two methods can be easily combined to boost their attack ability. Experimental results demonstrated that the proposed methods alone and together achieved false acceptance rates up to 73.29% and 88.79%, respectively, against Miura's hand-crafted FVR system. We also point out that Miura's system is easily compromised by non-vein-looking samples generated by a WGAN-GP model with false acceptance rates up to 94.21%. The results raise the alarm about the robustness of such systems and suggest that master vein attacks should be considered an important security measure.