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Face image authentication scheme based on MTCNN and SLT

Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Protecting Images From Manipulations With Deep Optical Signatures

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IEEE Journal on Selected Topics in Signal Processing 2025年第3期19卷 549-558页

作者： Arias, Kevin Gomez, Pablo Hinojosa, Carlos Niebles, Juan Carlos Arguello, Henry Universidad Industrial de Santander Department of System Engineering and Informatics Bucaramanga Colombia Center of Excellence in Generative AI Saudi Arabia Stanford University Salesforce Adjunct Professor of Computer Science CA United States

Due to the advancements in deep image generation models, ensuring digital image authenticity, integrity, and confidentiality becomes challenging. While many active image manipulation detection methods embed digital signatures post-image acquisition, the vulnerabilities persist if unauthorized access occurs before this embedding or the embedding software is compromised. This work introduces an optics-based active image manipulation detection approach that learns the structure of a color-coded aperture (CCA), which encodes the light within the camera and embeds a highly reliable and imperceptible optical signature before image acquisition. We optimize our camera model with our proposed image manipulation detection network via end-to-end training. We validate our approach with extensive simulations and a proof-of-concept optical system. The results show that our method outperforms the state-of-the-art active image manipulation detection techniques. © 2007-2012 IEEE.

关键词： Cameras

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Exploring Immersive Virtual Reality in Higher Education: Research Gap and Future Direction -A Scoping Review

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IEEE Access 2025年 13卷 76308-76321页

作者： Sunardi Meyliana Hendric, Spits Warnars Leslie Harco Rosmansyah, Yusep Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Information System Department School of Information System Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia

The rapid advancement of technology in the post-COVID-19 era has positioned immersive learning as a transformative approach to enhance educational experiences. Despite its vast potential, recent research developments reveal persistent challenges and gaps that impede widespread adoption. This study conducts a scoping review using the PRISMA methodology to systematically analyze current literature, identify research gaps, seek the challenge, and propose future research directions. From an initial pool of 414 papers, 75 were selected, comprising 60 research studies and 15 review papers. Notably, 55 studies focus on immersive virtual reality (IVR) purely for educational enhancement in traditional academic settings, while 20 explore the implementation of IVR in education with gaming activities. The analysis indicates a predominance of mixed-methods research within education, computer engineering, and computer science. Most studies are limited by short durations (typically 30 minutes) and small participant groups (under 50), raising concerns about the generalizability of findings. Key themes identified include learning context (21 papers), learning design strategies (10 papers), and immersion elements such as avatars and haptic feedback (six papers). While positive impacts like increased satisfaction, motivation, engagement, knowledge enhancement, and usability are reported, negative effects such as motion sickness (13 papers) and dizziness (11 papers) persist. Crucially, only 11 studies exhibit high statistical power, underscoring the need for more robust research designs. Challenges identified encompass participant limitations, homogeneity, user discomfort, hardware unfamiliarity, and cognitive load - all intricately linked to design strategies. The implications of this review highlight the necessity for future research to focus on long-term studies, optimize user experience, develop cost-effective content creation methods, and integrate gamification into learning desig

关键词： engineering education

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer Science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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A linear regression based prediction model for load distribution and quality of service improvement with different resource utilisation in cloud environment

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International Journal of Communication Networks and Distributed systems 2025年第3期31卷 277-317页

作者： Mandal, Gopa Dam, Santanu Dasgupta, Kousik Dutta, Paramartha Department of CSE Jalpaiguri Govt. Engineering College Jalpaiguri735102 India Department of HE Netaji Subhas Open University Salt Lake Kolkata700064 India Department of CSE Kalyani Govt. Engineering College Kalyani741235 India Department of Computer and System Sciences Visva- Bharati University Shantiniketan731 235 India

Cloud computing is a delivery-based consumption model that relies on the internet. The use of cloud enabled devices is increasing rapidly. So, to maintain quality of service (QoS), throughput of the entire system with service level agreements (SLAs) is a major concern between the service providers and the end users. Alternative techniques for virtual machine (VM) consolidation and proper workload allocation may be beneficial. This study proposes a linear regression-based prediction model for load distribution and QoS improvement. The model aims to enhance system throughput and QoS by predicting resource utilisation levels using historical consumption data. Experiments conducted using the CloudSim and CloudAnalyst platforms demonstrate positive results, outperforming existing methodologies. The study also evaluates service level agreement violation (SLAV) and delays to assess the QoS provided by the cloud service provider (CSP). Overall, this research contributes to the enhancement of QoS in cloud and cloud enabled systems like the Internet of Things and the Cloud of Things (CoT) and addresses the challenges of optimising resource utilisation while ensuring QoS. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Linear regression

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Artificial intelligence-based smart galliformes farm management system

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Journal of Umm Al-Qura University for engineering and Architecture 2025年第1期16卷 64-77页

作者： Himel, Galib Muhammad Shahriar Islam, Md. Masudul Kader, Md. Nasimul Rahman, Mustafizur School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Department of Computing and Information System Daffodil International Academy Dhaka Bangladesh Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computing and Information System Daffodil International University Dhaka Bangladesh

This article introduces an Artificial Intelligent-driven system for Galliformes Farm Management, consulting, and disease control. Comprising both a web-based mobile app and a website, the system integrates physical electronic devices to regulate intelligent automation. The application employs Artificial Neural Network and computer vision for the identification of breeds and the recognition of diseases in galliform birds, utilizing TensorFlow for real-time detection. It notifies local farmers about bird flu infections with an integrated infection map using Google's map Application Programming Interface. The system also controls farm temperature and humidity through microcontrollers and sensors, offering consulting features such as cost calculation, food chart generation, and drug suggestions. © The Author(s) 2025.

关键词： Artificial intelligence Breed identification Disease classification Image classification Poultry farm automation Smart farm management

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Collaborative Deadline-sensitive Multi-task Offloading in Vehicular-Cloud Networks 17

Collaborative Deadline-sensitive Multi-task Offloading in Ve...

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Kumar, Prasanna Sushma, S.A. Chandrasekaran, K. Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering Surathkal India Siddaganga Institute of Technology Cloud and Smart System Services Lab Tumakuru India

ISBN: (纸本)9798331531195

With the growing technological advancements in the Internet and advanced functionalities in vehicular networks, it becomes crucial to execute tasks quickly and efficiently. However, the limited onboard computational capacity and vehicle mobility make it challenging to accomplish latency-sensitive tasks efficiently. Task offloading provides a promising solution to overcome these challenges. Cloud data centers provide efficient solutions, but returning the results to the vehicles takes longer due to the large physical distance. Leveraging edge servers to execute latency-sensitive tasks provides a fast, interactive response and less transmission cost. However, in a dynamic network, vehicles will be in constant motion with varying speeds, resulting in frequent handoffs from one base station to another. Our proposed work aims to select the optimal nodes to perform binary offloading with minimum cost using the collaborative vehicular network. We use a greedy-based offloading approach to address these challenges and achieve better quality-of-service and quality-of-experience in a dynamic environment to minimize costs, delay reduction ratio, and satisfaction ratio. The proposed work outperforms the baseline by 60.44%, 53.43% in reducing total system cost, delay reduction ratio, and 36% improvement in the satisfaction ratio compared to baseline algorithms. © 2025 IEEE.

关键词： Quality of service

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SABiT-MNet: Scale-Adaptive Autoencoder with BiT-M Model for Identifying AMD Grades

SABiT-MNet: Scale-Adaptive Autoencoder with BiT-M Model for ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： El-Den, Niveen Nasr Elsharkawy, Mohamed Ghazal, Mohammed Mahmoud, Ali Sandhu, Harpal Mahdi, Hani El-Baz, Ayman Department of Computer and System Engineering Ain Shams University Egypt Department of Bioengineering University of Louisville LouisvilleKY United States Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350368741

Accurate early diagnosis is crucial in addressing Age-related Macular Degeneration (AMD), a chronic retinal disease that is a leading cause of blindness among the elderly. Medical imaging, particularly fundus imaging, is essential in facilitating timely detection and intervention. Due to the variability in image sizes within our dataset, this paper introduces the SABiT-MNet model, which effectively discriminates between healthy retinas, dry AMD, and wet AMD. The model integrates a novel scale-adaptive (SA) approach by combining an autoencoder with Big Transfer (BiT) as its backbone. Unlike traditional resizing methods, which often result in the loss of critical diagnostic information, the SA model dynamically adjusts to varying image sizes, preserving key retinal features essential for accurate diagnosis. The primary aim of this architecture is to retain crucial details in fundus images to ensure precise classification. In this study, 648 subjects were recruited through the Comparisons of AMD Treatments Trials study group, sponsored by the University of Pennsylvania. Experimental results demonstrate that the proposed SABiT-MNet model outperforms state-of-the-art approaches, including transformer-based models, achieving superior diagnostic accuracy. The model recorded performance metrics of 94% accuracy, 97% sensitivity, and 93.94% specificity. To further validate the robustness of the system, we tested it on the public ODiR dataset, where it achieved similarly promising results, confirming the effectiveness of our approach. © 2025 IEEE.

关键词： AMD Deep Learning fundus photography

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