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Survey on Video Security:Examining Threats,Challenges,and Future Trends

computers, Materials & Continua 2024年第9期80卷 3591-3635页

作者： Ali Asghar Amna Shifa Mamoona Naveed Asghar Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science College of Science and EngineeringUniversity of GalwayGalwayH91 TK33Ireland

Videos represent the most prevailing form of digital media for communication,information dissemination,and ***,theirwidespread use has increased the risks of unauthorised access andmanipulation,posing significant *** response,various protection approaches have been developed to secure,authenticate,and ensure the integrity of digital *** study provides a comprehensive survey of the challenges associated with maintaining the confidentiality,integrity,and availability of video content,and examining how it can be *** then investigates current developments in the field of video security by exploring two critical research ***,it examine the techniques used by adversaries to compromise video data and evaluate their *** these attack methodologies is crucial for developing effective defense ***,it explores the various security approaches that can be employed to protect video data,enhancing its transparency,integrity,and *** compares the effectiveness of these approaches across different use cases,including surveillance,video on demand(VoD),and medical videos related to disease ***,it identifies potential research opportunities to enhance video data protection in response to the evolving threat *** this investigation,this study aims to contribute to the ongoing efforts in securing video data,providing insights that are vital for researchers,practitioners,and policymakers dedicated to enhancing the safety and reliability of video content in our digital world.

关键词： Attacks threats security services video manipulation video security

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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Skin Disease Detection Using Neural Networks 2

Skin Disease Detection Using Neural Networks

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Gupta, Meenu Kumar, Rakesh Nandan Pradhan, Anil Kumar Obaid, Ahmed J. Chandigarh University Department of Computer Science & Engineering Punjab Mohali India University of Kufa Faculty of Computer Science and Mathematics Dept of Computer Science Najaf Iraq College of Engineering Uruk University Baghdad Iraq

ISBN: (纸本)9798350370188

Skin diseases like acne, psoriasis, eczema, and dermatitis affect millions worldwide. Skin cancer and melanoma are diseases that happen due to exposure to UV radiation. The early detection of skin diseases is crucial for effective treatment and prevention of further complications. This work comprehensively reviews various image-based techniques for skin disease detection, including traditional computer vision methods such as Convolutional Neural Network (CNN), Random Forest (RF), Naïve Bayes (NB), k-nearest Neighbour (k-NN), and Support Vector Machine (SVM). ISIC and dermofit datasets comprised 26,150 images divided into three categories: training (20,150), testing (3000), and validation (2000), respectively. Receiver Operating Characteristics (ROC) and Mean Squared Error (MSE) are used to calculate the accuracy and precision of models. As a result, it concluded that CNN outperformed with 94.91% accuracy. The proposed system can aid dermatologists in making accurate diagnoses and improve patient care. © 2024 IEEE.

关键词： Mean square error

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The Impact of CyRIS in Ascertaining Accurate Implementation of Projects 14

The Impact of CyRIS in Ascertaining Accurate Implementation ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharifi, Ahma Zia Walid, Md. Abul Ala Zia, Ghezal Ahmad Jan Salehin, Md. Nazmus Safi, Ihsan Ul Haq Al Islam, Iqbal Faculty of Computer Science Nangarhar Univeristy Department of Networking and Communication Afghanistan Department of Computer Science and Engineering Khulna9203 Bangladesh Berlin Germany Department of Computer Science and Engineering Natore Bangladesh Nangarhar University Department of Networking and Communication Faculty of Computer Science Afghanistan Department of Computer Science & Engineering Savar Dhaka Bangladesh

ISBN: (纸本)9798350335095

This systematic review seeks to evaluate the impact of CyRIS in ascertaining accurate implementation of projects with a primary focus on the merits of its functionalities. Core to the study is the view that CyRIS-driven project implementation reduces the risks of inaccuracy to an acceptable level. Although CyRIS is an extensively researched area in cyber security, a comprehensive review of subcategories, specifically, the accurate implementation of projects using the tool remains understudied. The proposed systematic review of literature provides insights and future directions from selected databases, including scienceDirect, Springer Journals, SAGE Journals, and Wiley Online Library. Moreover, the limitations and strengths of each article collected are assessed using the PRISMA model to identify risks of bias. The research, which consists of journal articles, conferences, and symposium papers, is analyzed using "Cyber-Range Instantiation systems and accurate implementation of projects"as the key phrase and "Cyber-Risk Models", "Cyber Range Instantiation", and "Cyber Security Training", and "Accurate Implementation of Projects"as the keywords. The findings of the study will bridge the awareness gap in using CyRIS-led Cyber-Risk Models to execute accurate training scenarios, improve weaknesses of implementation, simulate real-time attack scenarios, and highlight existing gaps as well as prospects for future research. © 2023 IEEE.

关键词： Cybersecurity

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Kardawi, Muhammad Yusuf Gunawan, Alexander Agung Santoso Bina Nusantara University Profesional Engineer Program Faculty of Engineering Jakarta11480 Indonesia Faculty of Computer Science University of Indonesia Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification. © 2025 IEEE.

关键词： Convolutional neural networks

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AI-Progressions in Automated Facial Paralysis Evaluation: A Comprehensive Survey of Traditional to Deep Learning Approaches 4

AI-Progressions in Automated Facial Paralysis Evaluation: A ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kiruthiga, S. Anbuananth, C. Dhilipkumar, V. Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331523923

Deep learning mechanisms allow computers to solve complex real-time problems with complex neural networks, it employs a vital role in health sector, for assisting the medical practitioners with quick and accurate decision making. Various studies on this research area proves that deep learning approaches are more effective in detecting facial paralysis, a common visible symptom of acute stroke. The sealing sign of stroke is facial weakness which destroys the newline muscle movements in the face on one or both sides of the nerves in the brain stream. Accurate and timely detection is most important and need of the golden hour, to patients effectively. Human assessment and traditional diagnostic methods have demonstrated poor inter rated reliability, demanding improved diagnostic tools. By analysing facial asymmetry and other relevant features like Face feature analysis, extracting characteristics such as eye shape, movement, and wrinkles from static images to detect facial weakness is done using various deep learning techniques. This work encompass the real-time challenges such as curating datasets, ethical concerns and data privacy of patients. The objective of this paper is to review the potentiality of various traditional to deep learning algorithms in automising the facial paralysis detection for accurate stroke diagnosis in patients. AI models can provide objective assessments that enhance the reliability of stroke detection. © 2025 IEEE.

关键词： Convolutional neural networks

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Real-Time Notifier for Blockchain Events 22

Real-Time Notifier for Blockchain Events

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22nd RoEduNet Conference: Networking in Education and Research, RoEduNet 2023

作者： Tanase-Paris, Cristian Carabas, Costin Tapus, Nicolae University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Bucharest Romania

ISBN: (纸本)9798350393651

Blockchain technology gained much traction in the last few years. These decentralized databases offer security, immutability, and scalability across various applications. Decentralized applications generate vast amounts of data, known as events, that are recorded on the blockchain and are public to anyone. Some people may see opportunities for financial gains in these events and would like to know when they occur. This paper proposes a solution to process and deliver those events as real-time alerts to the users. It uses existing technologies such as message queues, multithreading, and asynchronous processing and integrates them into a scalable architecture. The results we achieved in this paper show that for an evenly distributed network traffic, which does not entirely consists of transaction bursts, the proposed solution offers reliability, efficiency, and a suitable delivery time to those wishing to integrate it into their projects. With time, this solution, or improved architectures, may form the basis of the following big-data architectures for processing blockchain events. © 2023 IEEE.

关键词： Blockchain

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A Review of Multi-omics and Machine Learning: Advancements and Applications in Cancer Research 3

A Review of Multi-omics and Machine Learning: Advancements a...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Ayman, Hazem Bassem, Madonna Madkour, Malak Fawzi, Mariam Amr, Nada Yousry, Youmna Elgayar, Ziad Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem. © 2023 IEEE.

关键词： Gaussian distribution

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A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

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computers, Materials & Continua 2023年第3期74卷 5087-5103页

作者： Al-Hussien Seddik Mohammed Salah Gamal Behery Ahmed El-harby Ahmed Ismail Ebada Sokea Teng Yunyoung Nam Mohamed Abouhawwash Department of Computer Science Faculty of ScienceMinia UniversityMiniaEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaEgypt Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its ***,researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it;this is called the stego *** paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response(QR)code and maze game image *** system consists of two *** first component contains two processes,encryption process,and hiding *** encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game *** the other hand,the second component contains two processes,extraction and decryption,which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image,*** results were obtained using the bit error rate(BER)*** results confirmed that the system achieved high hiding capacity,good performance,and a high level of robustness against attackers compared with other coverless steganography methods.

关键词： Coverless steganography data hiding information security QR code maze game

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

Crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease Inception v3 CNN crop diseases

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