# Universal Material Translator: Towards Spoof Fingerprint Generalization

arXiv: https://arxiv.org/abs/1912.03737 · HTML: https://mlsanity.com/paper/1912.03737/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [SpoofGAN: Synthetic Fingerprint Spoof Images](https://mlsanity.com/paper/2204.06498/) (2022)
- [Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising](https://mlsanity.com/paper/2111.07620/) (2021)
- [A Unified Model for Fingerprint Authentication and Presentation Attack Detection](https://mlsanity.com/paper/2104.03255/) (2021)
- [Fingerprint Presentation Attack Detector Using Global-Local Model](https://mlsanity.com/paper/2402.12754/) (2024)
- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [Saliency-Guided Training for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2505.02176/) (2025)
- [Cascade Forgery Mining Network for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2607.24090/) (2026)

## Abstract (arXiv)

Spoof detectors are classifiers that are trained to distinguish spoof fingerprints from bonafide ones. However, state of the art spoof detectors do not generalize well on unseen spoof materials. This study proposes a style transfer based augmentation wrapper that can be used on any existing spoof detector and can dynamically improve the robustness of the spoof detection system on spoof materials for which we have very low data. Our method is an approach for synthesizing new spoof images from a few spoof examples that transfers the style or material properties of the spoof examples to the content of bonafide fingerprints to generate a larger number of examples to train the classifier on. We demonstrate the effectiveness of our approach on materials in the publicly available LivDet 2015 dataset and show that the proposed approach leads to robustness to fingerprint spoofs of the target material.