Deep Representations for Iris, Face, and Fingerprint Spoofing Detection

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, 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

A Survey on Deep Learning Techniques for Big Data in Biometrics

“…Experiments showed that these approaches gained outstanding classification results for all problems and modalities in eight out of nine benchmarks. The results describe that spoofing detection systems based on convolutional networks are robust to known attacks and can be adapted to image based attacks [9].…”

“…David et al [9] investigated spoofing detection systems for different biometric modalities such as iris, face, and fingerprint based on two deep learning approaches. The first approach makes use of learning suitable convolutional network architectures for each domain and the second approach focuses on learning the weights of the network via back-propagation.…”

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

“…Furthermore, by using sensory data generated from an infrared distance sensor, a deep learning classifier was developed for fall detection especially amongst the elderly population [50]. Besides these, with respect to security, combining biometric sensors and CNNs has resulted in a more robust approach for spoofing and security breach detection in digital systems [51]. Yin et al [52] used a deep convolution network for proper visual object recognition as another application area of deep learning in the analysis of big sensed data, whereas for early detection of deforestation, Barreto et al [53] proposed using a multilayer perceptron technique.…”

Detecção de spoofing facial: uma abordagem baseada nas máquinas de Boltzmann restritas

“…Entretanto, apesar da certa dificuldade em fraudar tais mecanismos de segurança em comparação com os sistemas baseados em senhas e cartões, hoje, dada sua disseminação pela sociedade, criminosos já desenvolveram mecanismos de ataque capazes de driblar os sensores de captura das características biométricas, simulando traços de usuários válidos, técnica conhecida como spoofing (MENOTTI et al, 2015;SILVA;PAULINO, 2015). Frente a esta realidade, torna-se necessário o desenvolvimento de métodos preventivos, istoé, de contramedida.…”