  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, while
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.
