Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/166040 |
Resumo: | 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. |
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Repositório Institucional da UNESP |
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Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack DetectionBiometric 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fed Univ Sao Carlos UFSCar, BR-13565905 Sao Carlos, SP, BrazilSao Paulo State Univ UNESP, BR-17033360 Bauru, SP, BrazilSao Paulo State Univ UNESP, BR-17033360 Bauru, SP, BrazilFAPESP: 2013/07375-0FAPESP: 2014/16250-9FAPESP: 2014/12236-1CNPq: 306166/2014-3IeeeUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Souza, Gustavo B.Santos, Daniel F. S. [UNESP]Pires, Rafael G.Marana, Aparecido N. [UNESP]Papa, Joao P. [UNESP]IEEE2018-11-29T09:28:10Z2018-11-29T09:28:10Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1863-1870application/pdf2017 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, p. 1863-1870, 2017.2161-4393http://hdl.handle.net/11449/166040WOS:000426968702015WOS000426968702015.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2017 International Joint Conference On Neural Networks (ijcnn)info:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/166040Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection |
title |
Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection |
spellingShingle |
Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection Souza, Gustavo B. |
title_short |
Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection |
title_full |
Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection |
title_fullStr |
Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection |
title_full_unstemmed |
Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection |
title_sort |
Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection |
author |
Souza, Gustavo B. |
author_facet |
Souza, Gustavo B. Santos, Daniel F. S. [UNESP] Pires, Rafael G. Marana, Aparecido N. [UNESP] Papa, Joao P. [UNESP] IEEE |
author_role |
author |
author2 |
Santos, Daniel F. S. [UNESP] Pires, Rafael G. Marana, Aparecido N. [UNESP] Papa, Joao P. [UNESP] IEEE |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Souza, Gustavo B. Santos, Daniel F. S. [UNESP] Pires, Rafael G. Marana, Aparecido N. [UNESP] Papa, Joao P. [UNESP] IEEE |
description |
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. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-11-29T09:28:10Z 2018-11-29T09:28:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2017 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, p. 1863-1870, 2017. 2161-4393 http://hdl.handle.net/11449/166040 WOS:000426968702015 WOS000426968702015.pdf |
identifier_str_mv |
2017 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, p. 1863-1870, 2017. 2161-4393 WOS:000426968702015 WOS000426968702015.pdf |
url |
http://hdl.handle.net/11449/166040 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2017 International Joint Conference On Neural Networks (ijcnn) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1863-1870 application/pdf |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1803047219856670720 |