Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks

Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters

Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013-2014 NIST i-vector machine learning challenge

On the Generalisation Capabilities of Fingerprint Presentation Attack Detection Methods in the Short Wave Infrared Domain

Nowadays, fingerprint-based biometric recognition systems are becoming increasingly popular. However, in spite of their numerous advantages, biometric capture devices are usually exposed to the public and thus vulnerable to presentation attacks (PAs). Therefore, presentation attack detection (PAD) methods are of utmost importance in order to distinguish between bona fide and attack presentations. Due to the nearly unlimited possibilities to create new presentation attack instruments (PAIs), unknown attacks are a threat to existing PAD algorithms. This fact motivates research on generalisation capabilities in order to find PAD methods that are resilient to new attacks. In this context, we evaluate the generalisability of multiple PAD algorithms on a dataset of 19,711 bona fide and 4,339 PA samples, including 45 different PAI species. The PAD data is captured in the short wave infrared domain and the results discuss the advantages and drawbacks of this PAD technique regarding unknown attacks

Evaluating the Sensitivity of Face Presentation Attack Detection Techniques to Images of Varying Resolutions

In the last decades, emerging techniques for face Presentation Attack Detection (PAD) have reported a remarkable performance to detect attack presentations whose attack type and capture conditions are known a priori. However, the generalisation capability of PAD approaches shows a considerable deterioration to detect unknown attacks. In order to tackle those generalisation issues, several PAD techniques have focused on the detection of homogeneous features from known attacks to detect unknown Presentation Attack Instruments without taking into account how some intrinsic image properties such as the image resolution or biometric quality could impact their detection performance. In this work, we carry out a thorough analysis of the sensitivity of several texture descriptors which shows how the use of images with varying resolutions for training leads to a high decrease on the attack detection performance

Privacy-preserving comparison of variable-length data with application to biometric template protection

The establishment of cloud computing and big data in a wide variety of daily applications has raised some privacy concerns due to the sensitive nature of some of the processed data. This has promoted the need to develop data protection techniques, where the storage and all operations are carried out without disclosing any information. Following this trend, this paper presents a new approach to efficiently compare variable-length data in the encrypted domain using homomorphic encryption where only encrypted data is stored or exchanged. The new variable-length-based algorithm is fused with existing fixed-length techniques in order to obtain increased comparison accuracy. To assess the soundness of the proposed approach, we evaluate its performance on a particular application: a multi-algorithm biometric template protection system based on dynamic signatures that complies with the requirements described in the ISO/IEC 24745 standard on biometric information protection. Experiments have been carried out on a publicly available database and a free implementation of the Paillier cryptosystem to ensure reproducibility and comparability to other schemes.This work was supported in part by the German Federal Ministry of Education and Research (BMBF); in part by the Hessen State Ministry for Higher Education, Research, and the Arts (HMWK) within the Center for Research in Security and Privacy (CRISP); in part by the Spanish Ministerio de Economia y Competitividad / Fondo Europeo de Desarrollo Regional through the CogniMetrics Project under Grant TEC2015-70627-R; and in part by Cecaban

KBOC: Keystroke Biometrics OnGoing Competition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents the first Keystroke Biometrics Ongoing evaluation platform and a Competition (KBOC) organized to promote reproducible research and establish a baseline in person authentication using keystroke biometrics. The ongoing evaluation tool has been developed using the BEAT platform and includes keystroke sequences (fixedtext) from 300 users acquired in 4 different sessions. In addition, the results of a parallel offline competition based on the same data and evaluation protocol are presented. The results reported have achieved EERs as low as 5.32%, which represent a challenging baseline for keystroke recognition technologies to be evaluated on the new publicly available KBOC benchmarkA.M. and M. G.-B. are supported by a JdC contract (JCI-2012- 12357) and a FPU Fellowship from Spanish MINECO and MCD, respectively. J.M. and J.C. are supported by CAPES and CNPq (grant 304853/2015-1). This work was partially funded by the projects: CogniMetrics (TEC2015-70627-R) from MINECO FEDER and BEAT (FP7-SEC-284989) from E