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.Comment: Pre-print of article that will appear in the IEEE Transactions on
Information Forenseics and Security (T.IFS), Special Issue on Biometric
Spoofing and Countermeasures, vol 10, n. 4, April 201
Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in:
AbstractAs a crucial security problem, anti-spoofing in biometrics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive in form of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks. The submitted propositions are evaluated on the Replay-Attack database and the achieved results are presented in this paper.
Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample (e.g., photograph, digital video, or even a 3D mask) to the acquisition sensor with the facial information of a valid user. In this paper, we introduce a low cost and software-based method for detecting spoofing attempts in face recognition systems. Our hypothesis is that during acquisition, there will be inevitable artifacts left behind in the recaptured biometric samples allowing us to create a discriminative signature of the video generated by the biometric sensor. To characterize these artifacts, we extract time-spectral feature descriptors from the video, which can be understood as a low-level feature descriptor that gathers temporal and spectral information across the biometric sample and use the visual codebook concept to find mid-level feature descriptors computed from the low-level ones. Such descriptors are more robust for detecting several kinds of attacks than the low-level ones. The experimental results show the effectiveness of the proposed method for detecting different types of attacks in a variety of scenarios and data sets, including photos, videos, and 3D masks.
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