# GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack Detection

arXiv: https://arxiv.org/abs/2105.10213 · HTML: https://mlsanity.com/paper/2105.10213/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [A Survey on GANs for Anomaly Detection](https://mlsanity.com/paper/1906.11632/) (2019)
- [Fingerprint Spoof Buster](https://mlsanity.com/paper/1712.04489/) (2017)
- [Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier](https://mlsanity.com/paper/1901.03918/) (2019)
- [Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing](https://mlsanity.com/paper/1907.04048/) (2019)
- [Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks](https://mlsanity.com/paper/1908.10163/) (2019)
- [RaspiReader: Open Source Fingerprint Reader](https://mlsanity.com/paper/1712.09392/) (2017)
- [Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015](https://mlsanity.com/paper/1609.01648/) (2016)
- [Anomaly Detection with Convolutional Autoencoders for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2008.07989/) (2020)
- [Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns](https://mlsanity.com/paper/1508.00537/) (2015)

## Built on by (most cited first, 3 of 3)

- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [Unsupervised Fingerphoto Presentation Attack Detection With Diffusion Models](https://mlsanity.com/paper/2409.18636/) (2024)

## Abstract (arXiv)

The need for reliable systems to determine fingerprint presentation attacks grows with the rising use of the fingerprint for authentication. This work presents a new approach to single-class classification for software-based fingerprint presentation attach detection. The described method utilizes a Wasserstein GAN to apply transfer learning to a deep convolutional autoencoder. By doing so, the autoencoder could be pretrained and finetuned on the LivDet2021 Dermalog sensor dataset with only 1122 bona fide training samples. Without making use of any presentation attack samples, the model could archive an average classification error rate of 16.79%. The Wasserstein GAN implemented to pretrain the autoencoders weights can further be used to generate realistic-looking artificial fingerprint patches. Extensive testing of different autoencoder architectures and hyperparameters led to coarse architectural guidelines as well as multiple implementations which can be utilized for future work.