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Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection

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Author(s):
Souza, Gustavo B. ; Santos, Daniel F. S. ; Pires, Rafael G. ; Marana, Aparecido N. ; Papa, Joao P. ; IEEE
Total Authors: 6
Document type: Journal article
Source: 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2017-01-01.
Abstract

Biometric systems present some important advantages over the traditional knowledge- or possess-oriented identification systems, such as a guarantee of authenticity and convenience. However, due to their widespread usage in our society and despite the difficulty in attacking them, nowadays criminals are already developing techniques to simulate physical, physiological and behavioral traits of valid users, the so-called spoofing attacks. In this sense, new countermeasures must be developed and integrated with the traditional biometric systems to prevent such frauds. In this work, we present a novel robust and efficient approach to detect spoofing attacks in biometric systems (fingerprint-based ones) using a deep learning-based model: the Deep Boltzmann Machine (DBM). By extracting and working with high-level features from the original data, DBM can deal with complex patterns and work with features that can not be easily forged. The results show the proposed approach outperforms other state-of-the-art techniques, presenting high accuracy in terms of attack detection and allowing working with less labeled data. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants