# An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network

arXiv: https://arxiv.org/abs/2306.03577 · HTML: https://mlsanity.com/paper/2306.03577/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Towards Principled Methods for Training Generative Adversarial Networks](https://mlsanity.com/paper/1701.04862/) (2017)
- [Unrolled Generative Adversarial Networks](https://mlsanity.com/paper/1611.02163/) (2016)
- [Fingerprint Spoof Buster](https://mlsanity.com/paper/1712.04489/) (2017)
- [LivDet in Action - Fingerprint Liveness Detection Competition 2019](https://mlsanity.com/paper/1905.00639/) (2019)
- [Improved generator objectives for GANs](https://mlsanity.com/paper/1612.02780/) (2016)
- [End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module](https://mlsanity.com/paper/1803.07830/) (2018)
- [Fingerprint liveness detection using local quality features](https://mlsanity.com/paper/1806.02974/) (2018)

## Built on by (most cited first, 2 of 2)

- [DyFFPAD: Dynamic Fusion of Convolutional and Handcrafted Features for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2308.10015/) (2023)
- [Cascade Forgery Mining Network for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2607.24090/) (2026)

## Abstract (arXiv)

The low-cost, user-friendly, and convenient nature of Automatic Fingerprint Recognition Systems (AFRS) makes them suitable for a wide range of applications. This spreading use of AFRS also makes them vulnerable to various security threats. Presentation Attack (PA) or spoofing is one of the threats which is caused by presenting a spoof of a genuine fingerprint to the sensor of AFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasure intended to protect AFRS against fake or spoof fingerprints created using various fabrication materials. In this paper, we have proposed a Convolutional Neural Network (CNN) based technique that uses a Generative Adversarial Network (GAN) to augment the dataset with spoof samples generated from the proposed Open Patch Generator (OPG). This OPG is capable of generating realistic fingerprint samples which have no resemblance to the existing spoof fingerprint samples generated with other materials. The augmented dataset is fed to the DenseNet classifier which helps in increasing the performance of the Presentation Attack Detection (PAD) module for the various real-world attacks possible with unknown spoof materials. Experimental evaluations of the proposed approach are carried out on the Liveness Detection (LivDet) 2015, 2017, and 2019 competition databases. An overall accuracy of 96.20%, 94.97%, and 92.90% has been achieved on the LivDet 2015, 2017, and 2019 databases, respectively under the LivDet protocol scenarios. The performance of the proposed PAD model is also validated in the cross-material and cross-sensor attack paradigm which further exhibits its capability to be used under real-world attack scenarios.