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IEEE Workshop on Applications of computer Vision (WACV)

作者： Shubhi Shukla Subhadeep Dalui Manaar Alam Shubhajit Datta Arijit Mondal Debdeep Mukhopadhyay Partha Pratim Chakrabarti Centre for Computational and Data Sciences IIT Kharagpur India Computer Science and Engineering Department IIT Kharagpur India Center for Cyber Security New York University Abu Dhabi UAE Department of Artificial Intelligence IIT Kharagpur India Computer Science and Engineering Department IIT Patna India

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Adversarial attacks rely on transferability, where an adversarial example (AE) crafted on a surrogate classifier tends to mislead a target classifier. Recent ensemble methods demonstrate that AEs are less likely to mislead multiple classifiers in an ensemble. This paper proposes a new ensemble training using a Pairwise Adversarially Robust Loss (PARL) that by construction produces an ensemble of classifiers with diverse decision boundaries. PARL utilizes outputs and gradients of each layer with respect to network parameters in every classifier within the ensemble simultaneously. PARL is demonstrated to achieve higher robustness against black-box transfer attacks than previous ensemble methods as well as adversarial training without adversely affecting clean example accuracy. Extensive experiments using standard Resnet20, WideResnet28-10 classifiers demonstrate the robustness of PARL against state-of-the-art adversarial attacks. While maintaining similar clean accuracy and lesser training time, the proposed architecture has a 24.8% increase in robust accuracy (∊= 0.07) from the state-of-the art method. Code is available at: https://***/shubhishukla10/PARL

关键词： Training computer vision Accuracy Perturbation methods Neural networks Closed box computer architecture Robustness Ensemble learning Standards

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Detection of the Multi-Scale Images Using SRP Network- A Review 3

Detection of the Multi-Scale Images Using SRP Network- A Rev...

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3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Mathur, Mansi Jindal, Vikas Wadhwa, Gitanjali Chandigarh University Department of Computer Science and Engineering Mohali India Kpriet Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9780738146270

Images are detected for a reason to provide better understandability to us. By seeing a picture one can analyze different factors or characteristics associated with it. There are various techniques of image text detection which are commonly used. The resolution of this paper is to sense multi-scale images with the help of SRPN (Scale-Based Region Proposal Network) or RPN. To achieve higher accuracy this method is used. It usually slows down the system's whole speed but the two staged frameworks which were proposed earlier yields good results, according to which first step includes the localization of wide scale ranged text and its efficient estimation. SRPN proposed only text regions by leaving non-text regions filtered out. Also, in light of content scale approximation by SRPN, little or large texts in area recommendations are resized into a bound together ordinary scale range. The second step involves fully CNN based text detector to localize every word from step one. © 2021 IEEE.

关键词： Location awareness Image resolution Lighting Estimation Object detection Detectors Color

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Detection of Behavioral Patterns of Viral Hepatitis Using data Mining

Detection of Behavioral Patterns of Viral Hepatitis Using Da...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Vaishali Gajendra Shende A. Jency K. Pavithra T. Jayasudha G. Venkatesan Manish Sharma Department of Computer Science Engineering East point college of Engineering and Technology Bangalore India Department of Artificial Intelligence and Data Science RMK College of Engineering and Technology Thiruvallur India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of English KPR Institute of Engineering and Technology Coimbatore India Department of Civil Engineering Saveetha Engineering College Chennai India Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun India

The data collected by the Information Systems related to Public Health are underutilized, mainly due to their large volume and their complexity. This work was applied to the Knowledge Discovery in databases technique based on the National Centre for Disease Control (NCDC), with the objective of tracing the epidemiological profile of viral hepatitis. Foram used the data referring to the corporation of Chennai in the year 2022, from two quais it was possible to compare the information obtained with the knowledge available in the specialized literature. Obtain a clear, simple and objective representation of the epidemiological profile of viral hepatitis through a decision tree and classification rules.

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Transformer doctor: diagnosing and treating vision transformers 24

Transformer doctor: diagnosing and treating vision transform...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiacong Hu Hao Chen Kejia Chen Yang Gao Jingwen Ye Xingen Wang Mingli Song Zunlei Feng College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Bangsheng Technology Co. Ltd. Electrical and Computer Engineering National University of Singapore College of Computer Science and Technology Zhejiang University and Bangsheng Technology Co. Ltd. College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision. However, the vast and complex parameters of Transformers impede researchers from gaining a deep understanding of their internal mechanisms, especially error mechanisms. Existing methods for interpreting Transformers mainly focus on understanding them from the perspectives of the importance of input tokens or internal modules, as well as the formation and meaning of features. In contrast, inspired by research on information integration mechanisms and conjunctive errors in the biological visual system, this paper conducts an in-depth exploration of the internal error mechanisms of Transformers. We first propose an information integration hypothesis for Transformers in the machine vision domain and provide substantial experimental evidence to support this hypothesis. This includes the dynamic integration of information among tokens and the static integration of information within tokens in Transformers, as well as the presence of conjunctive errors therein. Addressing these errors, we further propose heuristic dynamic integration constraint methods and rule-based static integration constraint methods to rectify errors and ultimately improve model performance. The entire methodology framework is termed as Transformer Doctor, designed for diagnosing and treating internal errors within transformers. Through a plethora of quantitative and qualitative experiments, it has been demonstrated that Transformer Doctor can effectively address internal errors in transformers, thereby enhancing model performance. For more information, please visit https://***/.

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Cognitive Defense Cyber Attack Prediction and Security Design in Machine Learning Model

Cognitive Defense Cyber Attack Prediction and Security Desig...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： P.T. Devadarshini B Chandrashekar Sumit Pundir Mohit Tiwari Ravikiran Madala E. Indhuma Department of Artificial Intelligence and Data Science Easwari Engineering College Chennai TamilNadu India Department Of Computer Science and Engineering St. Martin's Engineering College Kompalli Secunderabad Telangana Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina US Department of Computer Science and Engineering St.Martln's Engineering College Secunderabad Telangana India

Companies of all sizes in today's linked digital world are susceptible to cyber dangers. In this study, we provide a novel approach to cyber defence that integrates machine learning with cognitive defensive techniques to identify and prevent cyber attacks in their tracks. We built three ML models that use cognitive defensive strategies and other cutting-edge security measures like encryption and intrusion detection. A 95% accuracy rate was reached by our models, which is far greater than where things stand right now models. Our work has the potential to have a huge effect since the models we've developed may one day help businesses take a more preventative stance against cyber threats. Our findings may potentially be used to fields outside than cyber security. Overall, our study shows that machine learning and cognitive defense are useful for foreseeing and stopping cyber threats, and points the way toward fruitful future study in this area.

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Secured and Privacy-Preserving GPU-Based Machine Learning Inference in Trusted Execution Environment: A Comprehensive Survey

Secured and Privacy-Preserving GPU-Based Machine Learning In...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Arunava Chaudhuri Shubhi Shukla Sarani Bhattacharya Debdeep Mukhopadhyay Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

With the rapid advancement of machine learning (ML) models and their widespread application across various sectors such as intrusion detection, medical diagnosis, natural language processing, and autonomous driving, these technologies have achieved remarkable success. However, this progress has also raised significant concerns about ensuring the security of ML models and protecting both private training data and model outputs from getting exposed in a shared cloud environment. To address these challenges, researchers have proposed various methodologies to create privacy-preserving, secure, and trustworthy model execution environments to prevent adversarial attacks. This study provides a comprehensive review of Trusted Execution Environment (TEE) implementations across different hardware accelerators. It also offers an overview of modern techniques for preserving privacy and security in execution environments, while identifying critical research gaps that require attention. In essence, this survey is an important resource for researchers, providing insights into recent methodologies and guiding them to focus on pressing research challenges.

关键词： Surveys Deep learning Training Privacy data privacy Training data data models Stability analysis Security Hardware acceleration

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Novel methods for enhancing ECG visualization research

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Journal of Electrocardiology 2023年 78卷 15-16页

作者： Alahmadi, Alaa, PhD Davies, Alan, PhD Vigo, Markel, PhD Jay, Caroline, PhD College of Computer Science & Engineering at Yanbu Taibah University Medina Saudi Arabia Division of Informatics Imaging and Data Sciences School of Health Sciences The University of Manchester Manchester UK Department of Computer Science The University of Manchester Manchester UK

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Diagnosis of Plant Infection for Optimised Authomatic Health Monitoring System Using Vision Transformer Models

Diagnosis of Plant Infection for Optimised Authomatic Health...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Nitin Thapliyal Manisha Aeri Abhishek Satyarthi Vinay Kukreja Rishabh Sharma Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Artificial Intelligence & Data Science IIMT College of Engineering Greater Noida Uttar Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Mosaic Virus is highly responsible for the crop loss, especially the pepper, which is considered part of food crop, an essential aspect of world’s agriculture. This work aims at automating the classification of disease severity level-related the Pepper Leaf Mosaic Virus Disease threshold through vision transformer (ViT) model to ensure a critical job. The classification of the severity is more often accompanied by a fairly significant amount of workload and arbitrariness, resulting in equipment innovation in most of the locations. The work applies ViT-based approaches with sufficient precision to appropriately classify a dataset, which contains 10,800 meticulously selected photographs. The present model obtained a commendable average accuracy of 97.25%, which shows that the model was able to detect even smaller differences between the classes of severity. The operational criteria that include the tale of the precision, recall, and F1-score highlight the model’s ability to minimize to Feggsor false Positives with its ability to discriminate cases of viruses at different levels of the spectrum. The more advanced comparison charts made hand methodologies stand at the pinnacle in plant pathology studies. This study is not only beneficial in terms of illuminating the understanding about the Pepper Leaf Mosaic Virus but also gives farmers and researchers a transformative tool that could change the way disease control methods are used. Technologies AI that power ViT model are helping the agriculture industries throughout the world to implement sustainability and resiliency of the systems by providing responses in the right time and place in addressing viral infections.

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Unleashing The Power Of 6g Assisted Wireless Communication Network To Define A New Routing Strategy To Preserve Communication Energy 10

Unleashing The Power Of 6g Assisted Wireless Communication N...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Avinash, S. Sujatha, S. Poornima, D. Vedhashree, S.K. Saravanan, V. Malathi, P. St. Josephs College of Engineering Department of MBA Tamil Nadu Chennai India Sri Sairam College of Engineering Department of EEE Anekal Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India SIMATS Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350353778

The incorporation of cutting-edge technology into intelligent power grid management becomes a top priority as the globe keeps searching for effective and environmentally friendly energy solutions. The arrival of 6G wireless networks is expected to completely transform the methods used to track, manage, and enhance energy grids. An effort to provide high data speeds has already been made with the deployment of commercial fifth-generation (5G) wireless communications networks. The advent of IoT applications and the proliferation of smart devices will put a heavy strain on 5G wireless networks since they need extremely dependable and lowlatency connection. So, the massive continuous data traffic boom is probably going to be too much for 5G networks to handle. Research into enhancing current wireless networks in preparation for the 6G cellular systems, the next generation of mobile communication, has been spurred by this. This paper introduces the Neural 6G Wireless Routing Protocol (N6GWRP), a new routing protocol that supports intelligent communication. To evaluate the effectiveness of the suggested approach, it is cross-validated with the Multiple-Access Routing Protocol (MARP), a traditional routing model. Users should not experience any disruptions when mobile power and data transmission systems interact. Another major obstacle is connecting the vast array of devices and people with varying degrees of processing power in a way that is both sustainable and environmentally conscious. This study delves into the smart solution to the aforesaid difficulties and examines how 6G wireless networks might improve smart energy grid resilience and dependability through seamless interaction with other sophisticated technologies like blockchain. A more environmentally friendly and smart vision regarding electrical grid administration is possible thanks to the extensive study that strives to illuminate the revolutionary significance of 6G wireless technologies. © 2024 IEEE.

关键词： 5G mobile communication systems

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