| Full text | |
| Author(s): |
Menotti, David
[1]
;
Chiachia, Giovani
[1]
;
Pinto, Allan
[1]
;
Schwartz, William Robson
[2]
;
Pedrini, Helio
[1]
;
Falcao, Alexandre Xavier
[1]
;
Rocha, Anderson
[1]
Total Authors: 7
|
| Affiliation: | [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
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | IEEE Transactions on Information Forensics and Security; v. 10, n. 4, p. 864-879, APR 2015. |
| Web of Science Citations: | 136 |
| Abstract | |
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) | |
| FAPESP's process: | 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications |
| Grantee: | Luis Gustavo Nonato |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 13/04172-0 - Feature Learning for Natural Images Annotation |
| Grantee: | David Menotti Gomes |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 13/11359-0 - New Methods for Learning Deep Visual Hierarchies |
| Grantee: | Giovani Chiachia |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 10/05647-4 - Digital forensics: collection, organization, classification and analysis of digital evidences |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Grants - Young Investigators Grants |