Deep Boltzmann Machines for Robust Fingerprint Spoofing Attack Detection

Autor(es)

Souza, Gustavo B. ; Santos, Daniel F. S. ; Pires, Rafael G. ; Marana, Aparecido N. ; Papa, Joao P. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2017-01-01.

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. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático