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Efficient multimodal cancelable biometric system based on steganography and cryptography

Iran Journal of computer science 2023年第2期6卷 109-121页

作者： Salama, Gerges M. El-Gazar, Safaa Nassar, Rana M. El-Shafai, Walid Khalaf, Ashraf A. M. El-Banby, Ghada M. Hamed, Hesham F. A. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia 61111 Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh 11586 Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 84428 Saudi Arabia

Due to the development of hacking programs, it has become easy to penetrate systems. Hence, there is a need for strong security mechanisms. The use of traditional passwords has become insufficient to secure systems. Biometric authentication is now widely used for security applications, and it has proven to be superior compared to traditional authentication methods. However, two issues need to be considered in biometric systems. The first is not to keep biometric data in its original form in the database. If biometric traits are hacked, they will no longer be of use. Biometric data should be kept in cancelable forms for reuse. The second issue is the reliance on a single biometric, which limits the verification accuracy. This can be solved by using a multimodal biometric system. Using steganography and cryptography, this paper introduces a cancelable multimodal biometric system. As voiceprints, facial images, and fingerprint images are used. In this paper, the verification is performed through the Mel frequency cepstral coefficients (MFCCs) of the voiceprints. Steganography is used as a tool to secure features extracted from voiceprints by embedding them into the facial image using block-based singular value decomposition (BSVD). Double random phase encoding (DRPE) is utilized as an encryption algorithm to generate the final cancelable templates. To increase the level of system security, fingerprint images are used as random phase masks (RPMs). Verification is performed by estimating the correlation between registered and test MFCCs. The correlation value is then compared with a threshold value, which is calculated using the distribution curves for the genuine and imposter correlations. Equal error rate (EER) values close to zero and an area under the receiver operator characteristic curve (AROC) that is close to one are obtained from the simulation results, demonstrating the outstanding performance of the suggested system. The proposed system achieves good performan

关键词： BSVD Cancelable biometrics DRPE MFCCs

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A linear partitioning diversity metric for evaluation of permutation-based metaheuristic algorithms

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Evolutionary Intelligence 2025年第4期18卷 1-67页

作者： Shahbazi, Majid Safi-Esfahani, Faramarz Shahbazi, Sara Mirjalili, Seyedali Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran Big Data Research Center Najafabad Branch Islamic Azad University Najafabad Iran School of Information Systems and Modelling Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia University Research and Innovation Center Obuda University Budapest Hungary Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava Ostrava Czech Republic

Metaheuristic algorithms, implicitly or explicitly, typically aim to achieve a delicate balance between exploration and exploitation when improving a population of solutions for a given optimization problem. Diversity metrics are crucial indicators for both exploration and exploitation, which serves as a valuable tool for assessing the performance of metaheuristic algorithms. Although several metrics for diversity have been proposed, the majority of them are only suitable for problems with continuous space. However, many optimization problems are defined/modeled based on discrete variables, and permutation representation is widely used for modeling real-world problems. Many existing metrics are not applicable to permutation-based problems. Applying other existing metrics to such problems frequently encounters two primary drawbacks: computational cost and effectiveness to represent the actual diversity of the population. To address these drawbacks, a new exclusive diversity metric is proposed in this work for optimization problems modeled by permutations. This method calculates diversity by linearizing and partitioning the problem space based on the population distribution in the space and counting the number of individuals in the partitions. Compared to the eleven available metrics (including both permutation-based and usable non-permutation-based metrics) in the literature, the proposed method demonstrates superior performance, exhibiting acceptable accuracy, stability, sensitivity, and robustness, particularly in the presence of outliers and requiring minimal computational cost. The accuracy of the results of the method is, on average, 35% better than baseline metrics. The computational cost of the method is also nearly 50% better than the fastest available method. These characteristics make the metric a valuable mean for analyzing existing algorithms’ behavior or designing new ones. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of

关键词： Linear programming

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Optimal Placement of Edge-to-Cloud AR/VR Services with Reconfiguration Cost 17

Optimal Placement of Edge-to-Cloud AR/VR Services with Recon...

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17th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2024

作者： Herabad, Mohammadsadeq Garshasbi Taheri, Javid Ahmed, Bestoun S. Curescu, Calin Department of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Ericsson AB Stockholm Sweden

ISBN: (纸本)9798350367201

With the emergence of edge-to-cloud computing, determining the optimal service placement in these networks has become increasingly complex, and the problem is classified as NP-complete. This complexity is further amplified by the dynamic nature of the system and the high computational demands of multimedia processing. Maintaining optimal service placement under varying loads requires dynamic reconfiguration, which involves adjusting resource allocation or redeploying service components on the same resource. Although reconfiguration may cause disruptions to services and increase overhead, maintaining services in their current location can lead to substantial operational costs. This study investigates the optimal placement of service components with reconfiguration cost within the edge-to-cloud system when a portion of resources are already allocated to currently running services. We propose a Parallel Multi-Objective Genetic Algorithm (PMOGA) to determine an optimal service placement strategy, balancing placement and reconfiguration costs with efficient execution time. The results obtained from the implementations show that the PMOGA finds service placement strategies closer to optimal as the reconfiguration rate increases, but the reconfiguration cost also becomes more significant. Additionally, we parallelized the algorithm and significantly reduced its execution time. We believe that the findings in this study equip network providers with a valuable resource that allows them to plan optimal reconfigurations, leading to substantial improvements in service quality and agility. © 2024 IEEE.

关键词： Edge-to-cloud computing Multi-objective Genetic Algorithm Parallelism Reconfiguration Service placement

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Performance Comparison of dSPACE and MCU MPC Implementation in PV Power Optimizer

Performance Comparison of dSPACE and MCU MPC Implementation ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Metry, Morcos Guler, Naki Rohouma, Wesam Balog, Robert S. Texas A&m University Department of Electrical and Computer Engineering Doha Qatar Gazi University Department of Electrical-Electronic Engineering Technology Faculty Ankara Turkey University of Doha for Science and Technology Department of Electrical Engineering Doha Qatar Texas A&m University Department of Electrical and Computer Engineering College StationTX United States

ISBN: (纸本)9798350316445

Finite control set model predictive control (MPC) can be computationally intensive especially when scaled up for more complicated converters since each state variable in the system is evaluated for all possible switching configurations. Multi-objective optimization of the cost function adds to the complexity of MPC. Much of the MPC literature in power electronics relies on expensive rapid prototyping (RP) platforms (RPP) such as dSPACE and OPAL RT. This paper aims to bridge the gap between expensive RPP and low-cost MCUs by exploring the feasibility of rapid prototyping (RP) on the low-cost F28379D launchpad. This paper is aimed toward the practitioner or researcher who desires to reduce to practice the MPC methods developed on a RPP to a lower-cost embedded controller. The suitability of a low-cost Texas Instrument (TI) launchpad to perform the same rapid-prototyping functions needed for MPC regulator development in a full-featured, high-end RPP is discussed. Experimental results using the dSPACE 1006 and the F28379D Launchpad showing steady-state performance and dynamic response are illustrated and compared. Comparative results show the F28379D launchpad and dSPACE 1006 offer similar performance markers. © 2023 IEEE.

关键词： Low-cost controller Model Predictive Control Power Electronics rapid prototyping platforms

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Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation

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IEEE Access 2024年 12卷 36267-36279页

作者： Ngo, Ba Hung Lam, Ba Thinh Nguyen, Thanh Huy Dinh, Quang Vinh Choi, Tae Jong Chonnam National University Graduate School of Data Science Gwangju61186 Korea Republic of University of Science Faculty of Information Technology Ho Chi Minh City700000 Viet Nam National Cheng Kung University Department of Biomedical Engineering Tainan70101 Taiwan Vietnamese-German University School of Electrical Engineering and Computer Science Ho Chi Minh City72000 Viet Nam

The Vision Transformer (ViT) model serves as a powerful model to capture and comprehend global information, particularly when trained on extensive datasets. Conversely, the Convolutional Neural Network (CNN) model is beneficial to training with small datasets for retaining essential local information. Inspired by these properties of ViT and CNN models, we introduce a hybrid framework that smoothly increases the cross-domain generalization in Semi-supervised Domain Adaptation (SSDA). To achieve this goal, we first train the ViT model on abundant labeled source data, while the CNN model is trained on a few labeled target samples. Then, these models dynamically exchange their knowledge for potential generalization to unlabeled target data via the proposed method, named Dual Dynamic Consistency Regularization (D2CR). Specifically, the ViT model provides its pseudo labels to update the global perspective for the CNN model. Similarly, the CNN model offers pseudo labels to complement the local perspective for the ViT model. The previous methods use a fixed threshold algorithm for the pseudo-labeling process. However, we utilize the dynamic threshold strategy to create pseudo labels for the bi-directional consistency regularization learning between the ViT and CNN models. We verify our approach across several SSDA benchmark datasets. The outstanding experimental results provide strong evidence of the effectiveness and superiority of our approach over previous state-of-the-art SSDA methods. © 2013 IEEE.

关键词： Neural network models

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Design and Implementation of a Robotic Stone Machining System

Design and Implementation of a Robotic Stone Machining Syste...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Nguyen-Vinh, Khuong Hattotuwa, Dilsha Damruwan Dewasurendra, Hasindu RMIT University School of Science Engineering and Technology Ho Chi Minh City Viet Nam VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic

ISBN: (纸本)9798350331790

Industry 4.0 has brought about an era of digital transformation in smart manufacturing processes. Machining is one such vastly developing field with the application of industrial robots (IR) as a more efficient and flexible alternative to traditional CNC machines. Therefore, it is justifiable to study the different operating principles and mechanisms of a robotic machining system that has the potential to innovate this industry in a safe and automated environment. This paper highlights the key concepts involved in robot machining and details the design and implementation of a practical work cell incorporating a serial robot: ABB IRB 6700. The station utilizes an external rotating worktable, spindle, and a tool magazine among other hardware to achieve a fully operating machining, specifically cutting and milling station. First, the tools are assembled in an optimum configuration to support stone and marble milling tasks. Then, an offline simulation is carried out in a CAD/CAM environment to generate and optimize the robot path before using this data in Robotstudio to test the actual setup. As a result, a feasible and effective robotic stone machining system was developed with the capacity to substitute the widely used CNC machines in the near digital future. © 2023 IEEE.

关键词： 3D modeling

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in engineering & sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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Adversarial Learning Aided Malicious Network Traffic Detection Based on EDE Framework

Adversarial Learning Aided Malicious Network Traffic Detecti...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zhang, Lanping Wang, Yu Ohtsuki, Tomoaki Adebisi, Bamidele Wu, Hsiao-Chun Gui, Guan Njupt College of Telecommunications and Information Engineering Nanjing China Keio University Department of Information and Computer Science Yokohama Japan Manchester Metropolitan University Faculty of Science and Engineering Manchester United Kingdom Louisiana State University School of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350345384

With the increasing number of new malicious software and network attacks, anomalous network traffic detection technology needs to continuously identify unknown attacks in order to adapt to complex network environments. In this paper, we propose a novel adversarial learning aided malicious network traffic detection (AMD) method based on encoder-decoder-encoder (EDE) framework. The proposed AMD method detects unknown anomalies in a single type of semi-supervised form and converts network traffic data into pictures for later processing. First, an anomalous network traffic detection model consisting of two encoders, a decoder and a discriminator is introduced. Second, by using the visibility of normal samples and the unrecognizability of unknown anomaly samples, the difference between the two samples is enlarged to facilitate effective sample detection. Finally, experiments show that the proposed model detects anomalies in targeted space and is robust to complex and noisy network environments. At the same time, compared to traditional machine learning methods, AMD's shows performance improvement of up to 40%. © 2023 IEEE.

关键词： Anomaly detection

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Al-Shareeda, Sarah Ozguner, Fusun Redmill, Keith Duong, Trung Q. Canberk, Berk The Ohio State University Electrical and Computer Engineering Department United States Bts Group Turkey Istanbul Technical University Department of Ai and Data Engineering Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

ISBN: (纸本)9798350368369

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1]. © 2025 IEEE.

关键词： Authentication Deep Reinforcement Learning Identity-Based Cryptography Proximal Policy Optimization Task Offloading Vehicular Twin Networks

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Link Quality-Aware Geographic Predictive Routing for V2V Network Based on GPSR

Link Quality-Aware Geographic Predictive Routing for V2V Net...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Harayama, Michiko Mishioka, Masahiro Gifu University Faculty of Engineering Dept. Electrical Electronic and Computer Engineering Japan Grad. Sch. Natural Science and Technology Gifu University Dept. Intelligence Science and Engineering Japan

ISBN: (纸本)9798350326413

In recent years, advances in automotive IoT technology have not only improved automated driving and driving convenience but have also led to a diminishing number of traffic accidents. Vehicle Ad-hoc Networks (VANETs) have attracted much attention because automotive IoT is based on wireless communications. Ad-hoc routing protocols that apply the characteristics of vehicle-to-vehicle (V2V) networks are needed. One of them is Greedy Perimeter Stateless Forwarding (GPSR), a routing protocol using geographic information. It uses Global Positioning System (GPS) information for routing. However, GPSR may increase the packet drop rate due to radio attenuation and fading. Therefore, C. Li et al. proposed to select the next hop by evaluating the link quality. However, due to the large moving speed of the node, the next hop may be out of the radio propagation range at the communication timing. Therefore, in this study, we propose a new routing method that combines the future location prediction proposed by Z.S. Houssaini et al. The effectiveness of the method is demonstrated by simulations using ns-3 with traffic data acquired by SUMO. © 2023 IEEE.

关键词： Routing protocols

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