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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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Adaptive Flowing Traffic Prediction in Contention Random Access for Optimizing Virtual/Physical Resource in B5G/5G New Radio and Core Network

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IEEE Transactions on Network science and Engineering 2024年第2期11卷 1934-1946页

作者： Chang, Ben-Jye Lin, Yu-Ting National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu City64002 Taiwan

For differentiating and customizing different classes of traffic and virtualizing physical resources of networks and machines, B5G/5G specifies several novel mechanisms, including VNF, SDN, Service Function Chaining, Network Slicing, MEC, etc. In B5G/5G, the out-of-band contention based random access using PHY subframe preambles is specified before data transmissions. However, B5G/5G requires dynamically efficient approaches for realizing real 5G, including randomly contending limited sharing channel preambles, dynamically determining flow slicing of SFC, dynamically determining PM/VNFi/VNFc resources, while minimizing carrying cost and energy consumption via multi-link/multi-path 5G network. This paper thus proposes the efficient Adaptive Slice Flowing Prediction in Contention Random Access for Optimizing Virtual/Physical Resource in 5G (APROR) consisting of two phases: Dynamic Flow Random Contention of 5G (dFRC) and Adaptive Real-time Predictive Flow Data Rate of diverse types of slicing (aRPF). Numerical results show APROR outperforms all approaches in performance metrics: contention collision probability, access delay, E2E delay, loss, throughput, etc. Consequently, several contributions are definitely achieved: 1) to dynamically determine the preamble configuration modes and dynamic flow backoff, 2) to adaptively differentiate collision domains for different types of flows and minimize collision probability, and 3) to determine the optimal Virtual/Physical Resource achieving optimal SFC corresponding to network slices. © 2013 IEEE.

关键词： Real time systems

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FoodExpert: Portable Intelligent Device for Rapid Screening of Pulse Quality and Adulteration

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第1期2卷 2-11页

作者： Pandey, Harsh Arya, Subhanshu Das, Debanjan Udutalapally, Venkanna International Institute of Information Technology Naya Department of Electronics & Communication Engineering Naya Raipur493661 India National Institute of Technology Department of Computer Science & Engineering Warangal506004 India

Pulses are one of the most important food crops in the world due to their higher protein content, approximately 21%-25%. Therefore, it is crucial to analyze the crop's quality and impurity levels. Stones, pebbles, marble chips, and synthetic dyes, such as lead chromate, metanil yellow, and artificial colors, are some of the impurities added to pulse products, accidentally or on purpose. The existing analysis techniques are mostly laboratory-based, time-consuming, costly, and require human examination. To address this issue, this article presents an intelligent system, FoodExpert, based on image processing that automatically uses an image of a pulse sample to identify the region of interest and essential attributes. Then, machine learning frameworks are used to predict pulse quality and adulteration levels based on the obtained parameters. On the test dataset, the suggested model had a 96% accuracy rate for pulse quality prediction and 94% accuracy for adulteration level prediction. The model was successfully deployed on a Raspberry Pi-based hardware prototype and mobile application. © 2023 IEEE.

关键词： Feature extraction

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Identification of Corn Leaf Disease Using Deep Learning 16

Identification of Corn Leaf Disease Using Deep Learning

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16th IEEE International Conference on Computational Intelligence and communication Networks, CICN 2024

作者： Kaushik, Rekha Yagnik, Suryansh Verma, Aman Yadav, Nitin Indian Institute of Information Technology Department of Information Technology Bhopal India Indian Institute of Information Technology Department of Electronics and Communication Bhopal India Indian Institute of Information Technology Department of Computer Science Bhopal India

ISBN: (纸本)9798331505264

The agricultural production can be enhanced effectively by detecting corp diseases more efficiently thus, preventing crop failure due to contamination. This study utilized deep convolutional neural network (CNN) models to detect plant leaf diseases from leaf images. CNNs have demonstrated promising results for machine vision tasks. The models achieved accuracy rates ranging from 90.25% to 95.56% to classify the disease, surpassing traditional methods that rely on manually crafted features. Key CNN architectures explored include VGG16, ResNet50, and AlexNet, with optimizations such as batch size ad-justments, dropout incorporation, and varying training epochs. The high accuracy levels indicate that deep CNNs can reliably detect diseases, suggesting their potential for enabling automated crop disease detection in real-time agricultural systems. Moving forward, additional testing with expanded datasets of plant diseases could further validate the robustness of the models. Deep learning methods can be used to precisely predict the crop diseases, this approach may significantly aid efforts in early disease intervention and treatment, leading to improved crop health, yields, and ultimately, farm productivity. © 2024 IEEE.

关键词： Convolutional neural networks

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare 28th

Generating synthetic mixed-type tabular data by decoding sam...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Drapala, Jaroslaw Swiatek, Jerzy Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw50-370 Poland

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured variables in columns. Furthermore, the variables come in different types-some are numerical, while others are categorical. In this work, we introduce a novel method for constructing generators of synthetic tabular data with mixed types. The key point of our approach is the explicit utilization of a latent space to represent the original data. A case study using real medical data is presented. © 2024 The Authors.

关键词： Data privacy

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RecGuard: An efficient privacy preservation blockchain-based system for online social network users

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Blockchain(Research and Applications) 2023年第1期4卷 36-47页

作者： Samuel Akwasi Frimpong Mu Han Edward Kwadwo Boahen Rexford Nii Ayitey Sosu Isaac Hanson Otu Larbi-Siaw Isaac Baffour Senkyire Department of Computer Science and Technology Jiangsu University212013ZhenjiangChina Department of Computer Engineering Ghana Communication Technology UniversityGhana Department of Computer Science Ghana Communication Technology UniversityGhana Department of Telecom Engineering Ghana Communication Technology UniversityGhana School of Finance and Economics Jiangsu University 212013ZhenjiangChina

Recommendation systems provide ease and convenience for users to address information overload problems while interacting with online platforms such as social media and ***,it raises several questions about privacy,especially for users who prefer to remain anonymous,especially on online social networks(OSNs).Moreover,due to the commercialization of online users'data,some service providers sell users'data to third parties at the blind side of the users,which leads to trust issues between users and service *** matters call for a system that gives online users much-needed control and autonomy of their *** the advancement of blockchain technology,many research institutions are experimenting with decentralized technologies to resolve the OSN user dilemma of privacy intrusion against third parties and *** resolve these limitations,we propose RecGuard,a privacy preservation blockchain-based network *** developed two smart contracts,RG-SH and RG-ST,to ensure the security and privacy of user *** RG-SH manages user data,whereas the RGST stores data.A graph convolutional network(GCN)was integrated with the blockchain-based system to detect malicious ***,we implemented our framework prototype on a locally simulated *** analysis and experiment results show that the proposed scheme demonstrates the effectiveness and privacy of users in our framework.

关键词： Blockchain Privacy Online social network Recommender system

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A new CNN-based watermarking method for color medical images in a fusion domain

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Neural Computing and Applications 2025年 1-19页

作者： Hosny, Khalid M. Abdel-Aziz, Mostafa M. Lashin, Nabil A. Hamza, Hanaa M. Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Department of Information Technology Faculty of Information Technology and Computer Science Sinai University Arish Campus Al-Arish45518 Egypt

The transmission of medical images via medical agencies raises security concerns, necessitating increased security measures to ensure integrity and security. However, many watermarking algorithms overlook equipoise;the relation between robustness, invisibility, and payload capacity results in a less satisfactory performance. To bridge this gap, we propose a new blind watermarking method for securing medical images based on a convolutional neural network in a fusion domain. First, we transmute the host color image using a phase-only transform (PHOT) to detect the surface pattern. We feed the detected surface pattern into a pre-trained VGG19 model to impeccably extract the stable feature vector. We encrypt the extracted image features using a hybrid Chirikov map to enhance their robustness and reliability. The synergy between the pre-trained VGG19 model and PHOT in an integrated domain effectively captures additional native and localized image features. A hybrid Fibonacci Q-matrix method scrambles the binary watermark to enhance security. Integrating double encryption into the proposed method markedly enhances its resistance against diverse countermeasures. Experimental outcomes indicate that the proposed scheme performs efficiently in robustness and invisibility. The obtained PSNR value was up to 60.15 dB, which is optimal for human perception. The embedded watermark can be retrieved without any warping. The retrieved watermark seems to be authentic, exhibiting ideal BER and NC values. In almost all attack circumstances, the BER values approached zero while the NC values got closer. The proposed method demonstrates notable enhancements in robustness and invisibility compared to prior methods. © The Author(s) 2025.

关键词： Watermarking

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Exploring Unbiased Activation Maps for Weakly Supervised Tissue Segmentation of Histopathological Images

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2631-2642页

作者： Kang, Yuxin Li, Hansheng Shi, Xiaoshuang Zhang, Xiao Xing, Yaqiong Wen, Yuting Wang, Yi Cui, Lei Feng, Jun Yang, Lin Yulin University Department of Information Engineering Shaanxi Yulin China The School of Information Science and Technology Northwest University Shaanxi Xi'an China Northwest University School of Information Science and Technology Shaanxi Xi'an China University of Electronic Science and Technology of China Department of Computer Science and Engineering Sichuan Chengdu China Xi'An Jiaotong University School of Computer Science and Technology Shaanxi Xi'an China

Tissue segmentation in histopathological images plays a crucial role in computational pathology, owing to its significant potential to indicate the prognosis of cancer patients. Presently, numerous Weakly Supervised Semantic Segmentation (WSSS) methods strive to utilize image-level labels to achieve pixel-level segmentation, aiming to minimize the need for detailed annotations. Most of these methods rely on Class Activation Maps (CAM) extracted from classification models, frequently leading to poor coverage of objects. The major cause is attributed to the strong inductive bias of the classification model, focusing primarily on discriminative feature of objects, rather than non-discriminative features. Inspired by this, we propose a simple yet effective method that introduces a self-supervised task by exploiting both the discriminative and non-discriminative features, and generate Unbiased Activation Maps (UAM) to encompass the whole object. Specifically, our method entails clustering all spatial features of an object class to derive semantic centers. Each center then works as a spatial filter that amplifies similar feature and suppresses dissimilar feature, and extract high-quality pseudo-labels (some noise at object boundaries). Moreover, we further propose a Noise-Reduced (NR) Learning method to train the segmentation network towards credible signals and lessen the impact of false predictions. Comprehensive experimental results on two public histopathology image datasets demonstrate the superior performance of our method over the state-of-the-art weakly supervised segmentation methods. © 2025 IEEE. All rights reserved.

关键词： Semantic Segmentation

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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