# SpoofGAN: Synthetic Fingerprint Spoof Images

arXiv: https://arxiv.org/abs/2204.06498 · HTML: https://mlsanity.com/paper/2204.06498/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 69 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 27 of 27)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https://mlsanity.com/paper/1705.07761/) (2017)
- [Stabilizing Training of Generative Adversarial Networks through Regularization](https://mlsanity.com/paper/1705.09367/) (2017)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Learning a Fixed-Length Fingerprint Representation](https://mlsanity.com/paper/1909.09901/) (2019)
- [PrintsGAN: Synthetic Fingerprint Generator](https://mlsanity.com/paper/2201.03674/) (2022)
- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [Fingerprint Spoof Buster](https://mlsanity.com/paper/1712.04489/) (2017)
- [HERS: Homomorphically Encrypted Representation Search](https://mlsanity.com/paper/2003.12197/) (2020)
- [LivDet in Action - Fingerprint Liveness Detection Competition 2019](https://mlsanity.com/paper/1905.00639/) (2019)
- [Finger-GAN: Generating Realistic Fingerprint Images Using Connectivity Imposed GAN](https://mlsanity.com/paper/1812.10482/) (2018)
- [Enhanced Balancing GAN: Minority-class Image Generation](https://mlsanity.com/paper/2011.00189/) (2020)
- [High Fidelity Fingerprint Generation: Quality, Uniqueness, and Privacy](https://mlsanity.com/paper/2105.10403/) (2021)
- [Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier](https://mlsanity.com/paper/1901.03918/) (2019)
- [Level Three Synthetic Fingerprint Generation](https://mlsanity.com/paper/2002.03809/) (2020)
- [LivDet 2017 Fingerprint Liveness Detection Competition 2017](https://mlsanity.com/paper/1803.05210/) (2018)
- [Fingerprint Synthesis: Search with 100 Million Prints](https://mlsanity.com/paper/1912.07195/) (2019)
- [LivDet 2021 Fingerprint Liveness Detection Competition -- Into the unknown](https://mlsanity.com/paper/2108.10183/) (2021)
- [Fingerprint Presentation Attack Detection: Generalization and Efficiency](https://mlsanity.com/paper/1812.11574/) (2018)
- [SynFi: Automatic Synthetic Fingerprint Generation](https://mlsanity.com/paper/2002.08900/) (2020)
- [Universal Material Translator: Towards Spoof Fingerprint Generalization](https://mlsanity.com/paper/1912.03737/) (2019)
- [RaspiReader: Open Source Fingerprint Reader](https://mlsanity.com/paper/1712.09392/) (2017)
- [Fingerprint Presentation Attack Detection: A Sensor and Material Agnostic Approach](https://mlsanity.com/paper/2004.02941/) (2020)
- [Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks](https://mlsanity.com/paper/2201.06164/) (2022)
- [Multi-Modal Fingerprint Presentation Attack Detection: Evaluation On A New Dataset](https://mlsanity.com/paper/2006.07498/) (2020)
- [OCT Fingerprints: Resilience to Presentation Attacks](https://mlsanity.com/paper/1908.00102/) (2019)

## Built on by (most cited first, 17 of 17)

- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [AFR-Net: Attention-Driven Fingerprint Recognition Network](https://mlsanity.com/paper/2211.13897/) (2022)
- [Latent Fingerprint Recognition: Fusion of Local and Global Embeddings](https://mlsanity.com/paper/2304.13800/) (2023)
- [Synthetic Latent Fingerprint Generator](https://mlsanity.com/paper/2208.13811/) (2022)
- [Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions](https://mlsanity.com/paper/2404.13791/) (2024)
- [FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data](https://mlsanity.com/paper/2310.19024/) (2023)
- [A Dataless FaceSwap Detection Approach Using Synthetic Images](https://mlsanity.com/paper/2212.02571/) (2022)
- [Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis](https://mlsanity.com/paper/2404.17105/) (2024)
- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniques](https://mlsanity.com/paper/2403.13916/) (2024)
- [ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection](https://mlsanity.com/paper/2305.07602/) (2023)
- [Improving Contactless Fingerprint Recognition with Robust 3D Feature Extraction and Graph Embedding](https://mlsanity.com/paper/2409.08782/) (2024)
- [Conditional Synthetic Live and Spoof Fingerprint Generation](https://mlsanity.com/paper/2510.17035/) (2025)
- [Fair GANs through model rebalancing for extremely imbalanced class distributions](https://mlsanity.com/paper/2308.08638/) (2023)
- [RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images](https://mlsanity.com/paper/2308.09285/) (2023)
- [FingerSplat: Contactless Fingerprint 3D Reconstruction and Generation based on 3D Gaussian Splatting](https://mlsanity.com/paper/2509.15648/) (2025)
- [FVeinSyn: Synthetic Finger Vein Image Generator](https://mlsanity.com/paper/2608.27527/) (2026)

## Abstract (arXiv)

A major limitation to advances in fingerprint spoof detection is the lack of publicly available, large-scale fingerprint spoof datasets, a problem which has been compounded by increased concerns surrounding privacy and security of biometric data. Furthermore, most state-of-the-art spoof detection algorithms rely on deep networks which perform best in the presence of a large amount of training data. This work aims to demonstrate the utility of synthetic (both live and spoof) fingerprints in supplying these algorithms with sufficient data to improve the performance of fingerprint spoof detection algorithms beyond the capabilities when training on a limited amount of publicly available real datasets. First, we provide details of our approach in modifying a state-of-the-art generative architecture to synthesize high quality live and spoof fingerprints. Then, we provide quantitative and qualitative analysis to verify the quality of our synthetic fingerprints in mimicking the distribution of real data samples. We showcase the utility of our synthetic live and spoof fingerprints in training a deep network for fingerprint spoof detection, which dramatically boosts the performance across three different evaluation datasets compared to an identical model trained on real data alone. Finally, we demonstrate that only 25% of the original (real) dataset is required to obtain similar detection performance when augmenting the training dataset with synthetic data.