| Texto completo | |
| Autor(es): |
Menotti, David
[1]
;
Chiachia, Giovani
[1]
;
Pinto, Allan
[1]
;
Schwartz, William Robson
[2]
;
Pedrini, Helio
[1]
;
Falcao, Alexandre Xavier
[1]
;
Rocha, Anderson
[1]
Número total de Autores: 7
|
| Afiliação do(s) autor(es): | [1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil
[2] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270010 Belo Horizonte, MG - Brazil
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE Transactions on Information Forensics and Security; v. 10, n. 4, p. 864-879, APR 2015. |
| Citações Web of Science: | 136 |
| Resumo | |
Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or spoofed) and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, whereas the second approach focuses on learning the weights of the network via back propagation. We consider nine biometric spoofing benchmarks-each one containing real and fake samples of a given biometric modality and attack type- and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come. (AU) | |
| Processo FAPESP: | 11/22749-8 - Desafios em visualização exploratória de dados multidimensionais: novos paradigmas, escalabilidade e aplicações |
| Beneficiário: | Luis Gustavo Nonato |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 13/04172-0 - Aprendizado de Descritores para Anotação de Imagens Naturais |
| Beneficiário: | David Menotti Gomes |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 13/11359-0 - Novos Métodos para Aprendizado de Hierarquias Visuais Profundas |
| Beneficiário: | Giovani Chiachia |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 10/05647-4 - Computação forense e criminalística de documentos: coleta, organização, classificação e análise de evidências |
| Beneficiário: | Anderson de Rezende Rocha |
| Modalidade de apoio: | Auxílio à Pesquisa - Jovens Pesquisadores |