34 research outputs found
LivDet in Action - Fingerprint Liveness Detection Competition 2019
The International Fingerprint liveness Detection Competition (LivDet) is an
open and well-acknowledged meeting point of academies and private companies
that deal with the problem of distinguishing images coming from reproductions
of fingerprints made of artificial materials and images relative to real
fingerprints. In this edition of LivDet we invited the competitors to propose
integrated algorithms with matching systems. The goal was to investigate at
which extent this integration impact on the whole performance. Twelve
algorithms were submitted to the competition, eight of which worked on
integrated systems.Comment: Preprint version of a paper accepted at ICB 201
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
Fusion of fingerprint presentation attacks detection and matching: a real approach from the LivDet perspective
The liveness detection ability is explicitly required for current personal verification systems in many security applications. As a matter of fact, the project of any biometric verification system cannot ignore the vulnerability to spoofing or presentation attacks (PAs), which must be addressed by effective countermeasures from the beginning of the design process. However, despite significant improvements, especially by adopting deep learning approaches to fingerprint Presentation Attack Detectors (PADs), current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modelling the cause-effect relationships when two systems (spoof detection and matching) with non-zero error rates are integrated.
To solve this lack of investigations in the literature, we present in this PhD thesis a novel performance simulation model based on the probabilistic relationships between the Receiver Operating Characteristics (ROC) of the two systems when implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the editions of LivDet 2017-2019, the NIST Bozorth3, and the top-level VeriFinger 12.0 matchers. With the help of this simulator, the overall system performance can be predicted before actual implementation, thus simplifying the process of setting the best trade-off among error rates.
In the second part of this thesis, we exploit this model to define a practical evaluation criterion to assess whether operational points of the PAD exist that do not alter the expected or previous performance given by the verification system alone. Experimental simulations coupled with the theoretical expectations confirm that this trade-off allows a complete view of the sequential embedding potentials worthy of being extended to other integration approaches
Fingerprint Presentation Attacks: Tackling the Ongoing Arms Race in Biometric Authentication
The widespread use of Automated Fingerprint Identification Systems (AFIS) in consumer electronics opens for the development of advanced presentation attacks, i.e. procedures designed to bypass an AFIS using a forged fingerprint. As a consequence, AFIS are often equipped with a fingerprint presentation attack detection (FPAD) module, to recognize live fingerprints from fake replicas, in order to both minimize the risk of unauthorized access and avoid pointless computations. The ongoing arms race between attackers and detector designers demands a comprehensive understanding of both the defender’s and attacker’s perspectives to develop robust and efficient FPAD systems. This paper proposes a dual-perspective approach to FPAD, which encompasses the presentation of a new technique for carrying out presentation attacks starting from perturbed samples with adversarial techniques and the presentation of a new detection technique based on an adversarial data augmentation strategy. In this case, attack and defence are based on the same assumptions demonstrating that this dual research approach can be exploited to enhance the overall security of fingerprint recognition systems against spoofing 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
Fingerprint recognition with embedded presentation attacks detection: are we ready?
The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more “secure” and whether the technology available is ready, and, if so, at what operational working conditions. Despite significant improvements, especially by adopting deep learning approaches to fingerprint PAD, current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modeling the cause-effect relationships when two non-zero error-free systems work together. Accordingly, this paper explores the fusion of PAD into verification systems by proposing a novel investigation instrument: a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the most recent editions of LivDet (2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger 12 matchers. Reported experiments explore significant scenarios to get the conditions under which fingerprint matching with embedded PAD can improve, rather than degrade, the overall personal verification performance
An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network
The low-cost, user-friendly, and convenient nature of Automatic Fingerprint
Recognition Systems (AFRS) makes them suitable for a wide range of
applications. This spreading use of AFRS also makes them vulnerable to various
security threats. Presentation Attack (PA) or spoofing is one of the threats
which is caused by presenting a spoof of a genuine fingerprint to the sensor of
AFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasure
intended to protect AFRS against fake or spoof fingerprints created using
various fabrication materials. In this paper, we have proposed a Convolutional
Neural Network (CNN) based technique that uses a Generative Adversarial Network
(GAN) to augment the dataset with spoof samples generated from the proposed
Open Patch Generator (OPG). This OPG is capable of generating realistic
fingerprint samples which have no resemblance to the existing spoof fingerprint
samples generated with other materials. The augmented dataset is fed to the
DenseNet classifier which helps in increasing the performance of the
Presentation Attack Detection (PAD) module for the various real-world attacks
possible with unknown spoof materials. Experimental evaluations of the proposed
approach are carried out on the Liveness Detection (LivDet) 2015, 2017, and
2019 competition databases. An overall accuracy of 96.20\%, 94.97\%, and
92.90\% has been achieved on the LivDet 2015, 2017, and 2019 databases,
respectively under the LivDet protocol scenarios. The performance of the
proposed PAD model is also validated in the cross-material and cross-sensor
attack paradigm which further exhibits its capability to be used under
real-world attack scenarios
DyFFPAD: Dynamic Fusion of Convolutional and Handcrafted Features for Fingerprint Presentation Attack Detection
Automatic fingerprint recognition systems suffer from the threat of
presentation attacks due to their wide range of applications in areas including
national borders and commercial applications. Presentation attacks can be
performed by fabricating the fake fingerprint of a user with or without the
intention of the subject. This paper presents a dynamic ensemble of deep
learning and handcrafted features to detect presentation attacks in
known-material and unknown-material protocols. The proposed model is a dynamic
ensemble of deep CNN and handcrafted features empowered deep neural networks
both of which learn their parameters together. The proposed presentation attack
detection model, in this way, utilizes the capabilities of both classification
techniques and exhibits better performance than their individual results. The
proposed model's performance is validated using benchmark LivDet 2015, 2017,
and 2019 databases, with an overall accuracy of 96.10\%, 96.49\%, and 95.99\%
attained on them, respectively. The proposed model outperforms state-of-the-art
methods in benchmark protocols of presentation attack detection in terms of
classification accuracy.Comment: arXiv admin note: text overlap with arXiv:2305.0939