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Tiffanie Edwards

Bio: Tiffanie Edwards is an academic researcher from Southern Connecticut State University. The author has contributed to research in topics: Completeness (order theory) & Cryptocurrency. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Papers
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Journal ArticleDOI
04 Mar 2021
TL;DR: A framework for multibiometric systems is developed, which combines a deep learning technique with a serial fusion method and improves accuracy by leveraging deep learning technology in feature extraction and score generation.
Abstract: We develop a framework for multibiometric systems, which combines a deep learning technique with the serial fusion method. Deep learning techniques have been used in unimodal and parallel fusion-based multimodal biometric systems in the past few years. While deep learning techniques have been successful in improving the authentication accuracy, a biometric system is still challenged by two issues: 1) a unimodal system suffers from environmental interference, spoofing attacks, and nonuniversality, and 2) a parallel fusion-based multimodal system suffers from user inconvenience as it requires the user to provide multiple biometrics, which in turn takes longer verification times. A serial fusion method can improve user convenience in a multibiometric system by requiring a user to submit only a subset of the available biometrics. To our knowledge, the effectiveness of using a deep learning technique with a serial fusion method in multibiometric systems is still underexplored. In this article, we close this research gap. We develop a three-stage multibiometric system using a user's fingerprint, palm, and face and test three serial fusion methods with a Siamese neural network. Our experiments achieve an AUC of 0.9996, where the genuine users require only 1.56 biometrics (instead of all 3) on an average. Impact statement— We work on enhancing the user convenience and reducing the verification error in a multibiometric system. An improved multibiometric system can help law enforcement, homeland security, defense, and our daily lives by providing better access control. With the advent of deep learning technologies, the accuracy of multibiometric systems have been improved significantly; however, its applicability is still in question because of long verification times required by parallel fusion in a multibiometric system. Our proposed multibiometric framework alleviates this user inconvenience issue by utilizing a serial fusion strategy in decision making and improves accuracy by leveraging deep learning technology in feature extraction and score generation.

14 citations

Journal ArticleDOI
TL;DR: BlockQuery as discussed by the authors is a proof-of-concept blockchain query system for Bitcoin that is designed from a forensic standpoint and meets all four of the defined querying criteria of being open source, confidential, automatically converting key representations, and allowing the manual adjustment of derivation depth.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors provide a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses.
Abstract: Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.

125 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses.
Abstract: Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.

75 citations

Journal ArticleDOI
TL;DR: Analysis of zoom touchscreen gestures proves that zoom gestures demonstrate promise for use in continuous smartphone authentication and identification applications.

5 citations

Journal ArticleDOI
TL;DR: In this paper , a deep learning convolutional neural network (DLCNN) was used to implement the robust multi-modal biometric identification and verification of human beings based on their physiological or behavioral characteristics.

5 citations

Journal ArticleDOI
01 Jul 2022-Sensors
TL;DR: A simple feature-fusion method that integrates features extracted by using pre-trained, deep learning models with more traditional colour and texture features with the aim of improving face presentation attack detection.
Abstract: Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literature shows that both modern, pre-trained, deep learning-based methods, and traditional hand-crafted, feature-engineered methods have been effective in detecting PAs. However, the question remains as to whether features learned in existing, deep neural networks sufficiently encompass traditional, low-level features in order to achieve optimal performance on PA detection tasks. In this paper, we present a simple feature-fusion method that integrates features extracted by using pre-trained, deep learning models with more traditional colour and texture features. Extensive experiments clearly show the benefit of enriching the feature space to improve detection rates by using three common public datasets, namely CASIA, Replay Attack, and SiW. This work opens future research to improve face presentation attack detection by exploring new characterizing features and fusion strategies.

4 citations