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Storing and Processing
- Information in Connectionist Systems,” in Eckmiller, R., ed., Advanced Neural Computers
, 1990
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Combining Match Scores with Liveness Values in a Fingerprint Verification System
"... We discuss the problem of combining biometric match scores with liveness measure values in the context of fingerprint verification. Recent literature has focused on the development of methods to assess if an input fingerprint sample is a “live ” entity or a “spoof ” artefact. This is commonly done b ..."
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We discuss the problem of combining biometric match scores with liveness measure values in the context of fingerprint verification. Recent literature has focused on the development of methods to assess if an input fingerprint sample is a “live ” entity or a “spoof ” artefact. This is commonly done by generating a single-valued numerical entity referred to as the liveness measure value. However, the problem of combining this liveness value with match scores has not been rigorously investigated. The goal of this work is to design a framework in which a liveness detector is incorporated with a fingerprint matcher. We first design and analyze three different methods to combine match scores with liveness values. Next, we introduce a Bayesian Belief Network (BBN) scheme that models the relationship between match scores and liveness values. Experiments carried out on a publicly available database of the Fingerprint Liveness Detection Competition 2009 (LivDet09) show the effectiveness of assuming a certain degree of influence of liveness values on match scores. 1.
LBP-TOP based countermeasure against facial spoofing attacks
- in International Workshop on Computer VisionWith Local Binary Pattern Variants - ACCV (Daejeon, Korea
, 2012
"... Abstract. User authentication is an important step to protect informa-tion and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech ..."
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Abstract. User authentication is an important step to protect informa-tion and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech cheap equipments. This article presents a countermeasure against such attacks based on the LBP −TOP operator combining both space and time information into a single multiresolution texture descrip-tor. Experiments carried out with the REPLAY ATTACK database show a Half Total Error Rate (HTER) improvement from 15.16 % to 7.60%. 1
Can face anti-spoofing countermeasures work in a real world scenario
- in Proc. IEEE Int. Conf. on Biometrics (ICB
, 2013
"... User authentication is an important step to protect in-formation and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech equipment ..."
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User authentication is an important step to protect in-formation and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech equipments. This article assesses how well existing face anti-spoofing countermeasures can work in a more realistic condition. Experiments carried out with two freely avail-able video databases (Replay Attack Database and CASIA Face Anti-Spoofing Database) show low generalization and possible database bias in the evaluated countermeasures. To generalize and deal with the diversity of attacks in a real world scenario we introduce two strategies that show promising results. 1.
Analysis of User-specific Score Characteristics for Spoof Biometric Attacks
"... Several studies in biometrics have confirmed the existence of user-specific score characteristics for genuine and zero-effort impostor score distributions. As an important consequence, biometric users contribute disproportionately to the FRR (false reject rate) and FAR (false accept rate) of the sys ..."
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Several studies in biometrics have confirmed the existence of user-specific score characteristics for genuine and zero-effort impostor score distributions. As an important consequence, biometric users contribute disproportionately to the FRR (false reject rate) and FAR (false accept rate) of the system. This phenomena is also know as the Doddington zoo effect. Recent studies indicate the vulnerability of unimodal and multibiometric systems to spoof attacks. The aim of this study is to analyze the score characteristics for spoof attacks. Such an analysis will 1) help improve our understanding of the Doddington zoo effect under spoof attacks; and 2) allow us to design biometric classifiers that are more robust to such attacks. The contributions of this paper are as follows: a) examining the existence of user-specific score characteristics for spoof attacks and b) analyzing the correlation between user-specific score characteristics obtained on genuine (as well as zero-effort impostor) and non zero-effort impostor (spoof) score distributions. Experiments conducted on the LivDet09 spoofed fingerprint database confirms that biometric user-groups exhibit different degrees of vulnerability to spoof attacks as well. Further, moderate negative correlation may exist between users who are difficult to recognize and their vulnerability to spoof attacks. 1.
Robustness analysis of Likelihood Ratio score fusion rule for multimodal biometric systems under spoofing attacks
- Int. Carnahan Conf. Security and Technology
, 2011
"... Abstract-Recent works have shown that, contrary to a common belief, multi-modal biometric systems may be "forced" by an impostor by submitting a spoofed biometric replica of a genuine user to only one of the matchers. Although those results were obtained under a worst-case scenario when t ..."
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Abstract-Recent works have shown that, contrary to a common belief, multi-modal biometric systems may be "forced" by an impostor by submitting a spoofed biometric replica of a genuine user to only one of the matchers. Although those results were obtained under a worst-case scenario when the attacker is able to replicate the exact appearance of the true biometric, this raises the issue of investigating more thoroughly the robustness of multimodal systems against spoof attacks and devising new methods to design robust systems against them. To this aim, in this paper we propose a robustness evaluation method which takes into account also scenarios more realistic than the worst-case one. Our method is based on an analytical model of the score distribution of fake traits, which is assumed to lie between the one of genuine and impostor scores, and is parametrised by a measure of the relative distance to the distribution of impostor scores, we name "fake strength". Varying the value of such parameter allows one to simulate the different factors which can affect the distribution of fake scores, like the ability of the attacker to replicate a certain biometric. Preliminary experimental results on real bimodal biometric data sets made up of faces and fingerprints show that the widely used LLR rule can be highly vulnerable to spoof attacks against one only matcher, even when the attack has a low fake strength.
Evaluation of multimodal biometric score fusion rules under spoof attacks
- in 2012 5th IAPR International Conference on Biometrics (ICB), 2012
"... Recent works have shown that multimodal biometric sys-tems can be evaded by spoofing only a single biometric trait. In this paper, we propose a method to evaluate the robust-ness of such systems against spoofing attacks, when score-level fusion rules are used. The aim is to rank several score-level ..."
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Recent works have shown that multimodal biometric sys-tems can be evaded by spoofing only a single biometric trait. In this paper, we propose a method to evaluate the robust-ness of such systems against spoofing attacks, when score-level fusion rules are used. The aim is to rank several score-level fusion rules, to allow the designer to choose the most robust one according to the model predictions. Our method does not require to fabricate fake biometric traits, and al-lows one to simulate different possible spoofing attacks us-ing the information of genuine and impostor distributions. Reported results, using data set containing realistic spoof-ing attacks, show that our method can rank correctly score-level fusion rules under spoofing attacks. 1.
Combining Multiple Iris Matchers using Advanced Fusion Techniques to Enhance Iris Matching Performance By
, 2014
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o Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. o NonCommercial — You may not use the material for commercial purposes. o ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. How to cite this thesis
Identifying Security Evaluation of Pattern Classifiers Under attack
"... ABSTRACT-Pattern classification is a branch of machine learning that focuses on recognition of patterns and regularities in data. In adversarial applications like biometric authentication, spam filtering, network intrusion detection the pattern classification systems are used. As this adversarial s ..."
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ABSTRACT-Pattern classification is a branch of machine learning that focuses on recognition of patterns and regularities in data. In adversarial applications like biometric authentication, spam filtering, network intrusion detection the pattern classification systems are used. As this adversarial scenario is not taken into account by classical design methods, pattern classification systems may exhibit vulnerabilities, whose exploitation may severely affect their performance, and consequently limit their practical utility. Extending pattern classification theory and design methods to adversarial settings is thus a novel and very relevant research direction, which has not yet been pursued in a systematic way. We propose a framework for evaluation of pattern security,model of adversary for defining any attack scenario. Reported results show that security evaluation can provide a more complete understanding of the classifier's behavior in adversarial environments, and lead to better design choices KEYWORDS: Adversarial classification, adversarial scenario, performance evaluation, security evaluation. I.INTRODUCTION In Pattern classification systems machine learning algorithms are used to perform security-related applications like biometric authentication, network intrusion detection, and spam filtering, to distinguish between a "legitimate" and a "malicious" pattern class. The input data can be purposely manipulated by an adversary to make classifiers to produce false negative. Contrary to traditional ones, these Applications have an intrinsic adversarial nature since the input data can be purposely manipulated by an intelligent and adaptive adversary to undermine classifier operation. This often gives rise to an arms race between the adversary and the classifier designer. Well known examples of attacks against pattern classifiers are: submitting a fake biometric trait to a biometric authentication system (spoofing attack) [ identifying potential vulnerabilities of machine learning algorithms during learning and classification; devising appropriate attacks that correspond to the identified threats and evaluating their impact on the targeted system; Proposing countermeasures to improve the security of machine learning algorithms against the considered attacks.
Pattern Recognition Letters xxx (2013) xxx–xxx Contents lists available at SciVerse ScienceDirect Pattern Recognition Letters
"... journal homepage: www.elsevier.com/locate/patrec Efficient software attack to multimodal biometric systems and its ..."
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journal homepage: www.elsevier.com/locate/patrec Efficient software attack to multimodal biometric systems and its
