# DeFraudNet:End2End Fingerprint Spoof Detection using Patch Level Attention

arXiv: https://arxiv.org/abs/2002.08214 · HTML: https://mlsanity.com/paper/2002.08214/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [MoSFPAD: An end-to-end Ensemble of MobileNet and Support Vector Classifier for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2303.01465/) (2023)
- [ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection](https://mlsanity.com/paper/2305.07602/) (2023)
- [EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection](https://mlsanity.com/paper/2103.02207/) (2021)
- [EXPRESSNET: An Explainable Residual Slim Network for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2305.09397/) (2023)

## Abstract (arXiv)

In recent years, fingerprint recognition systems have made remarkable advancements in the field of biometric security as it plays an important role in personal, national and global security. In spite of all these notable advancements, the fingerprint recognition technology is still susceptible to spoof attacks which can significantly jeopardize the user security. The cross sensor and cross material spoof detection still pose a challenge with a myriad of spoof materials emerging every day, compromising sensor interoperability and robustness. This paper proposes a novel method for fingerprint spoof detection using both global and local fingerprint feature descriptors. These descriptors are extracted using DenseNet which significantly improves cross-sensor, cross-material and cross-dataset performance. A novel patch attention network is used for finding the most discriminative patches and also for network fusion. We evaluate our method on four publicly available datasets:LivDet 2011, 2013, 2015 and 2017. A set of comprehensive experiments are carried out to evaluate cross-sensor, cross-material and cross-dataset performance over these datasets. The proposed approach achieves an average accuracy of 99.52%, 99.16% and 99.72% on LivDet 2017,2015 and 2011 respectively outperforming the current state-of-the-art results by 3% and 4% for LivDet 2015 and 2011 respectively.