144 research outputs found
Two-Factor Authentication Approach Based on Behavior Patterns for Defeating Puppet Attacks
Fingerprint traits are widely recognized for their unique qualities and
security benefits. Despite their extensive use, fingerprint features can be
vulnerable to puppet attacks, where attackers manipulate a reluctant but
genuine user into completing the authentication process. Defending against such
attacks is challenging due to the coexistence of a legitimate identity and an
illegitimate intent. In this paper, we propose PUPGUARD, a solution designed to
guard against puppet attacks. This method is based on user behavioral patterns,
specifically, the user needs to press the capture device twice successively
with different fingers during the authentication process. PUPGUARD leverages
both the image features of fingerprints and the timing characteristics of the
pressing intervals to establish two-factor authentication. More specifically,
after extracting image features and timing characteristics, and performing
feature selection on the image features, PUPGUARD fuses these two features into
a one-dimensional feature vector, and feeds it into a one-class classifier to
obtain the classification result. This two-factor authentication method
emphasizes dynamic behavioral patterns during the authentication process,
thereby enhancing security against puppet attacks. To assess PUPGUARD's
effectiveness, we conducted experiments on datasets collected from 31 subjects,
including image features and timing characteristics. Our experimental results
demonstrate that PUPGUARD achieves an impressive accuracy rate of 97.87% and a
remarkably low false positive rate (FPR) of 1.89%. Furthermore, we conducted
comparative experiments to validate the superiority of combining image features
and timing characteristics within PUPGUARD for enhancing resistance against
puppet attacks
Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey
The vulnerabilities of fingerprint authentication systems have raised
security concerns when adapting them to highly secure access-control
applications. Therefore, Fingerprint Presentation Attack Detection (FPAD)
methods are essential for ensuring reliable fingerprint authentication. Owing
to the lack of generation capacity of traditional handcrafted based approaches,
deep learning-based FPAD has become mainstream and has achieved remarkable
performance in the past decade. Existing reviews have focused more on
hand-cratfed rather than deep learning-based methods, which are outdated. To
stimulate future research, we will concentrate only on recent
deep-learning-based FPAD methods. In this paper, we first briefly introduce the
most common Presentation Attack Instruments (PAIs) and publicly available
fingerprint Presentation Attack (PA) datasets. We then describe the existing
deep-learning FPAD by categorizing them into contact, contactless, and
smartphone-based approaches. Finally, we conclude the paper by discussing the
open challenges at the current stage and emphasizing the potential future
perspective.Comment: 29 pages, submitted to ACM computing survey journa
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Biometrics in ABC: counter-spoofing research
Automated border control (ABC) is concerned with fast and secure processing for intelligence-led identification. The
FastPass project aims to build a harmonised, modular reference system for future European ABC. When biometrics is taken on
board as identity, spoofing attacks become a concern. This paper presents current research in algorithm development for
counter-spoofing attacks in biometrics. Focussing on three biometric traits, face, fingerprint, and iris, it examines possible types
of spoofing attacks, and reviews existing algorithms reported in relevant academic papers in the area of countering measures to
biometric spoofing attacks. It indicates that the new developing trend is fusion of multiple biometrics against spoofing attacks
Security and accuracy of fingerprint-based biometrics: A review
Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper
Security and accuracy of fingerprint-based biometrics: A review
Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper
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