1,081 research outputs found
Privacy-Preserving Facial Recognition Using Biometric-Capsules
Indiana University-Purdue University Indianapolis (IUPUI)In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design.
In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods
Comprehensive Survey: Biometric User Authentication Application, Evaluation, and Discussion
This paper conducts an extensive review of biometric user authentication
literature, addressing three primary research questions: (1) commonly used
biometric traits and their suitability for specific applications, (2)
performance factors such as security, convenience, and robustness, and
potential countermeasures against cyberattacks, and (3) factors affecting
biometric system accuracy and po-tential improvements. Our analysis delves into
physiological and behavioral traits, exploring their pros and cons. We discuss
factors influencing biometric system effectiveness and highlight areas for
enhancement. Our study differs from previous surveys by extensively examining
biometric traits, exploring various application domains, and analyzing measures
to mitigate cyberattacks. This paper aims to inform researchers and
practitioners about the biometric authentication landscape and guide future
advancements
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
Biometrics for internetâofâthings security: A review
The large number of InternetâofâThings (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometricâbased authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometricâcryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the stateâofâtheâart research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forwardâlooking issues and future research directions
Reversing the Irreversible: A Survey on Inverse Biometrics
With the widespread use of biometric recognition, several issues related to
the privacy and security provided by this technology have been recently raised
and analysed. As a result, the early common belief among the biometrics
community of templates irreversibility has been proven wrong. It is now an
accepted fact that it is possible to reconstruct from an unprotected template a
synthetic sample that matches the bona fide one. This reverse engineering
process, commonly referred to as \textit{inverse biometrics}, constitutes a
severe threat for biometric systems from two different angles: on the one hand,
sensitive personal data (i.e., biometric data) can be derived from compromised
unprotected templates; on the other hand, other powerful attacks can be
launched building upon these reconstructed samples. Given its important
implications, biometric stakeholders have produced over the last fifteen years
numerous works analysing the different aspects related to inverse biometrics:
development of reconstruction algorithms for different characteristics;
proposal of methodologies to assess the vulnerabilities of biometric systems to
the aforementioned algorithms; development of countermeasures to reduce the
possible effects of attacks. The present article is an effort to condense all
this information in one comprehensive review of: the problem itself, the
evaluation of the problem, and the mitigation of the problem. The present
article is an effort to condense all this information in one comprehensive
review of: the problem itself, the evaluation of the problem, and the
mitigation of the problem.Comment: 18 pages, journal, surve
Multi-Modal Biometrics: Applications, Strategies and Operations
The need for adequate attention to security of lives and properties cannot be over-emphasised. Existing approaches to security management by various agencies and sectors have focused on the use of possession (card, token) and knowledge (password, username)-based strategies which are susceptible to forgetfulness, damage, loss, theft, forgery and other activities of fraudsters. The surest and most appropriate strategy for handling these challenges is the use of naturally endowed biometrics, which are the human physiological and behavioural characteristics. This paper presents an overview of the use of biometrics for human verification and identification. The applications, methodologies, operations, integration, fusion and strategies for multi-modal biometric systems that give more secured and reliable human identity management is also presented
FedBiometric: Image Features Based Biometric Presentation Attack Detection Using Hybrid CNNs-SVM in Federated Learning
In the past few years, biometric identification systems have become popular for personal, national, and global security. In addition to other biometric modalities, facial and fingerprint recognition have gained popularity due to their uniqueness, stability, convenience, and cost-effectiveness compared to other biometric modalities. However, the evolution of fake biometrics, such as printed materials, 2D or 3D faces, makeup, and cosmetics, has brought new challenges. As a result of these modifications, several facial and fingerprint Presentation Attack Detection methods have been proposed to distinguish between live and spoof faces or fingerprints. Federated learning can play a significant role in this problem due to its distributed learning setting and privacy-preserving advantages. This work proposes a hybrid ResNet50-SVM based federated learning model for facial Presentation Attack Detection utilizing Local Binary Pattern (LBP), or Gabor filter-based extracted image features. For fingerprint Presentation Attack Detection (PAD), this work proposes a hybrid CNN-SVM based federated learning model utilizing Local Binary Pattern (LBP), or Histograms of Oriented Gradient (HOG)-based extracted image features
Enhancing Biometric-Capsule-based Authentication and Facial Recognition via Deep Learning
In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based authentication systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based BioCapsule method. The BioCapsule method is provably secure, privacy-preserving, cancellable and flexible in its secure feature fusion design. In this work, we extend BioCapsule to face-based recognition. Moreover, we incorporate state-of-art deep learning techniques into a BioCapsule-based facial authentication system to further enhance secure recognition accuracy. We compare the performance of an underlying recognition system to the performance of the BioCapsule-embedded system in order to demonstrate the minimal effects of the BioCapsule scheme on underlying system performance. We also demonstrate that the BioCapsule scheme outperforms or performs as well as many other proposed secure biometric techniques
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