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Deep Learning-Based Countermeasures for Fingerprint Spoof Detection
Amal Abdulrahman Almuarik, Mashael Aldughayem, Munirah Alshathri, Nouf Alrowais, Ouiem Bchir
Pages - 1 - 24     |    Revised - 31-03-2025     |    Published - 30-04-2025
Volume - 18   Issue - 1    |    Publication Date - April 2025  Table of Contents
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KEYWORDS
Biometric Verification Systems, Fingerprint Spoof Attacks, Genuine Fingerprint, Spoof Fingerprint, Deep Learning, ResNet, GoogLeNet, DenseNet, Vision Transformer.
ABSTRACT
Biometric systems have become a crucial part of modern life, offering secure and seamless authentication. However, as people increasingly share their biometric data, they may not fully understand the associated risks. Fingerprint-based security systems, while popular, are vulnerable to spoofing attacks where counterfeit fingerprints made from materials like latex, silicone, or gelatin can deceive the system. This paper addresses the need for improved security by proposing a new countermeasure for detecting such spoof attacks. The solution utilizes deep learning models to differentiate between genuine and spoof fingerprints. Four models—ResNet, GoogLeNet, DenseNet, and Vision Transformer—were evaluated. The study found that ResNet34 and DenseNet169 consistently outperformed the other models on the ATVS and LivDet2015 datasets, achieving higher True Detection Rates (TDR) at a False Detection Rate (FDR) of 0.05, as well as better F1 scores and Average Classification Error (ACE). Additionally, these models demonstrated lower time complexity, making them both effective and efficient for detecting fingerprint spoofing. This research highlights the importance of incorporating advanced deep learning techniques to enhance the reliability and security of fingerprint authentication systems.
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Miss Amal Abdulrahman Almuarik
Faculty of Computer and Information Sciences, Computer Science Department, King Saud University, Riyadh - Saudi Arabia
amalalmuarik@gmail.com
Miss Mashael Aldughayem
Faculty of Computer and Information Sciences, Computer Science Department, King Saud University, Riyadh - Saudi Arabia
Miss Munirah Alshathri
Faculty of Computer and Information Sciences, Computer Science Department, King Saud University, Riyadh - Saudi Arabia
Miss Nouf Alrowais
Faculty of Computer and Information Sciences, Computer Science Department, King Saud University, Riyadh - Saudi Arabia
Professor Ouiem Bchir
Faculty of Computer and Information Sciences, Computer Science Department, King Saud University, Riyadh - Saudi Arabia


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