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检索条件"主题词=Detection Algorithms"

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Deep Fake detection Based on Hybrid Approach Involving Residual Network Deep Learning Architecture

Deep Fake Detection Based on Hybrid Approach Involving Resid...

引用

IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： N.V. Shibu R. Muthunagai S. Prabu Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India Department of Electrical & Electronics Engineering Manakula Vinayagar Institute of Technology Puducherry India Department of Computing Technologies School of computing SRM Institute of Science and Technologies Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

This Deep fake Technology aims at generating synthetic video content or morphed video content which is very realistic to the naked eye. It involves the use of deep learning algorithms in the domain of video processing. In most cases, such artificially generated video contents are very difficult to get differentiated from naturally captured video signals. The existing methodologies of detection involved image tamper detection and digital forensics algorithms. These algorithms are ineffective in detecting videos tampered using deep fake technology. The proposed technique involves training and building of deep learning models that works like a black box to detect the artefacts crucial in the detection of video tampering. This paper presents an overview of the technology advancements in the field of deep fake video detection techniques over recent years. This paper also performs a comparative analysis on the performance of hybrid algorithms involving residual network deep learning architecture in the detection of video signals morphed using deep fake technology. The training datasets are obtained from Face forensics to build the model. The algorithm is augmented with image quality assessment, temporal consistency analysis and facial landmarks analysis to improve the detection algorithm. The performance metrics involve the generation of confusion matrix and generation of accuracy measures. The results provide a promising direction towards the construction of better detection models to safeguard against current and future threats.

关键词： Training Deep learning Measurement Image quality Deepfakes Current measurement Digital forensics detection algorithms Faces Residual neural networks

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Efficient Anomaly detection Algorithm for Operational Data Based on Fuzzy Cognitive Map

Efficient Anomaly Detection Algorithm for Operational Data B...

引用

Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), International Conference on

作者： Xin Zhao Changda Huang Marketing Service Center of State Grid Xinjiang Electric Power Co. LTD Urumqi China

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

In order to improve the accuracy and efficiency of anomaly detection in operational data, an efficient anomaly detection algorithm for operational data based on fuzzy cognitive maps is proposed. Clean, denoise, and standardize raw operational data; Extracting concept nodes based on fuzzy cognitive maps; Constructing a fuzzy cognitive map model based on relational mapping to obtain the probability distribution results of operational data; Design an efficient anomaly detection algorithm for operational data through big data information fusion and association rule mining methods. The test results show that this method can improve the sensitivity and detection rate of abnormal data detection, reduce false detection rate, KL divergence, and average detection delay time. The accuracy and efficiency of abnormal detection in operational data are high.

关键词： Accuracy Sensitivity Fuzzy cognitive maps Probability distribution Data models Association rule learning Delays Internet of Things detection algorithms Anomaly detection

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Community detection for Large Graphs on GPUs with Unified Memory

Community Detection for Large Graphs on GPUs with Unified Me...

引用

IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Emre Dinçer Iŝil Öz Computer Engineering Izmir Institute of Technology Izmir Turkey

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

While GPUs accelerate applications from different domains with different characteristics, processing large datasets gets infeasible on target systems with limited device memory. Unified memory support makes it possible to work with data larger than available GPU memory. However, page migration overhead for executions with irregular memory access patterns, like graph processing workloads, induces severe performance degradation. While memory hints help to deal with page movements by keeping data in suitable memory spaces, coarse-grain configurations can still not avoid migrations for executions having diverse data structures. In this work, we target the state-of-the-art CUDA implementation of the Louvain community detection algorithm and evaluate the impacts of the fine-grained unified memory hints on the performance. Our experimental evaluation shows that memory hints configured for specific data structures reveal significant performance improvements and enable us to work efficiently with large graphs.

关键词： Performance evaluation Degradation Codes Runtime Memory management Graphics processing units Data structures detection algorithms

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Heavy Hitter Flow detection using P4-based Programmable Data Plane Switches

Heavy Hitter Flow Detection using P4-based Programmable Data...

引用

International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Ks Adarsha Krishna M. Sivalingam Gauravdeep Shami Marc Lyonnais Rodney Wilson Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India External Research Global Research and Development Ciena Corporation Ottawa Canada

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

In data networks carrying large numbers of flows, Heavy Hitters (HHs) or Elephant flows are the flows exceeding pre-determined thresholds (e.g. wrt. number of packets or bytes) in a given time window. Such HH flows need to be handled differently in order to minimize their impact on other smaller flows. In recent years, HH detection techniques were shown to be effectively implementable in programmable data plane switches. In recent work, it was shown that the inter-packet gap can be used to identify heavy hitters. In order to conserve memory, such schemes use a limited-size hash table for storing flow state information and using this for the detection. However, when hash collisions occur, it is possible that a valid HH flow in the table can be replaced by a non-HH flow resulting in missing detection of HH flows. To address this problem, this paper incorporates a flow’s medium term Packet Count feature. In order to limit the packet count field size in the hash table, counting is done only till Hash Collision occurs so as to the reduce the range of values to be stored and thus, the required number of bits. The proposed scheme has been implemented in the P4 language and run on Intel Tofino hardware. Performance evaluation has been done using MAWI-based real-life traffic traces. The results shows that in several scenarios cases, we can significantly reduce the False Negatives for HHs by using the packet count data effectively and efficiently.

关键词： Performance evaluation Flow production systems Telecommunication traffic Feature extraction Hardware Computer networks Telecommunications detection algorithms

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DriSafePh: An IoT Based Realtime Driver Drowsiness detection System using Hybrid Machine learning Algorithm

DriSafePh: An IoT Based Realtime Driver Drowsiness Detection...

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International Conference on Software Technology and Engineering (ICSTE)

作者： Kenneth Von C. Golosinda Jasper Van D. Marcellones Luke Jellergil P. Ampongol Neil P. Magloyuan Patrick D. Cerna Mapua Malayan Colleges Mindanao Davao City Philippines

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

Driver drowsiness contributes significantly to road accidents worldwide, and while drowsiness detection systems have already been implemented on higher-end cars, DriSafePh introduces an embedded system using a Raspberry Pi that can be implemented in any enclosed vehicle. DriSafePh aims to address the prevalent issue of driver drowsiness on the road by developing a real-time driver drowsiness detection system and leveraging a hybrid machine learning algorithm that processes facial landmarks and gestures to accurately detect drowsiness, which has yet to be used in its field and will be compared to other algorithms already in use. Additionally, the researcher aims to provide a warning notification to the driver through a text-to-speech algorithm when the appropriate drowsiness level is detected and optimize the machine learning model's performance. The study conducted a survey to gather drivers' opinions on a drowsiness detection system and their personal experiences with drowsy driving. The results showed that the respondents viewed the working prototype positively. Additionally, the data revealed that more than 70% of the respondents had experienced drowsy driving, highlighting the need for such a device. The recommended approach of the DriSafePh model was evaluated using a performance matrix, and the detection for the face, eye, and mouth provides a high accuracy level of 96-99%. In the meantime, eye closure and yawn detection accuracy were able to match the other existing algorithms on the Raspberry Pi device with an accuracy of 96.89% and 85.93%, respectively.

关键词： Performance evaluation Surveys Machine learning algorithms Accuracy Software algorithms Machine learning Software Text to speech detection algorithms Vehicles

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Research on a SAR Image Ship detection Algorithm Based on Feature Refinement

Research on a SAR Image Ship Detection Algorithm Based on Fe...

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Zhou Yucheng Gong Yan Yao Zhixuan China Waterborne Transport Research Institute Beijing China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

SAR images have long been plagued by coherent speckle noise and challenges in detecting small ships within the field of ship detection. This paper introduces a synthetic aperture radar (SAR) image ship detection algorithm based on feature refinement. The algorithm employs a denoising module to mitigate noise interference and captures more spatial position information through a hierarchical feature fusion (HFF) module, enhancing the detection precision of small ships. Lastly, the algorithm leverages a feature refinement (FR) module based on deformable convolution to minimize clutter signal interference, thus enhancing the accuracy of ship positioning. Experimental results demonstrate that the algorithm exhibits high accuracy and robustness in detecting ships from SAR images.

关键词： Accuracy Convolution Noise Noise reduction Speckle Feature extraction Radar polarimetry Robustness Marine vehicles detection algorithms

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An Effective Image Copy-Move Forgery detection Using Entropy Information

An Effective Image Copy-Move Forgery Detection Using Entropy...

引用

International Conference on Image, Vision and Computing (ICIVC)

作者： Li Jiang Zhaowei Lu School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

Image forensics has become increasingly crucial in our daily lives. Among various types of forgeries, copy-move forgery detection has received considerable attention within the academic community. Keypoint-based algorithms, particularly those based on Scale Invariant Feature Transform, have achieved promising outcomes. However, most of keypoint detection algorithms failed to generate sufficient matches when tampered patches were occurred in smooth areas, leading to insufficient matches. Therefore, this paper introduces entropy images to determine the coordinates and scales of keypoints based on Scale Invariant Feature Transform detector, which make the pre-processing more suitable for solving the above problems. Furthermore, an overlapped entropy level clustering algorithm is developed to mitigate the increased matching complexity caused by the non-ideal distribution of gray values in keypoints. Experimental results demonstrate that our algorithm achieves a good balance between performance and time efficiency.

关键词： Image forensics Clustering algorithms Transforms Detectors Gray-scale Feature extraction Entropy Forgery Complexity theory detection algorithms

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An Efficient Flow Rule Conflict Comprehensive detection Scheme for SDN Networks

An Efficient Flow Rule Conflict Comprehensive Detection Sche...

引用

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Bo Han Yuan Liu Yongbin Zhou Yiwen Gao School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Software-Defined Networking (SDN) has introduced flexibility and efficiency to network management but also faces challenges from flow rule conflicts, including static, dynamic, and dependency conflicts. Existing detection algorithms often focus on a single conflict type, resulting in inefficiencies and high false positive rates. To address these issues, we propose a comprehensive flow rule conflict detection scheme that improves real-time detection of explicit conflicts (both static and dynamic) and reduces false positives in implicit (dependency) conflict detection. Specifically, we present a real-time explicit conflict detection algorithm based on the Protocol-Divided Trie (PDT), which categorizes flow rules by protocol type and uses a prefix tree for rapid matching. Experimental results show that this approach significantly reduces detection times by at least 39.2% and achieves 100% detection accuracy. Additionally, we propose a two-stage detection (TSD) algorithm that combines the precision of path-based detection (PBD) with the efficiency of alias set-based detection (ASD). Our experiments reveal a 51% reduction in false positives compared to ASD and a 48% reduction in detection time compared to PBD, while maintaining equivalent false positive rates. This approach provides a robust solution for conflict detection in SDN, improving network security and resource utilization efficiency.

关键词： Distributed processing Protocols Accuracy Network security Real-time systems Resource management Software defined networking detection algorithms Faces

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Impolite Words detection Algorithm for Standard Thai and Thai Regional Dialects

Impolite Words Detection Algorithm for Standard Thai and Tha...

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International Computer Science and Engineering Conference (ICSEC)

作者： Kanida Charungchit Nannapas Abhiratthanarath Chouvalit Khancome Department of Information Technology Faculty of Science and Technology Rajabhat Rajanagarindra University Chachoengsao Thailand Department of Computer Scienc Faculty of Science Ramkhamhaeng University Bangkok Thailand

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

Impolite word detection, also known as rude word detection, is a natural language processing (NLP) technique designed to identify inappropriate or offensive language in texts and various media. This technique aims to prevent the use of language that could be considered inappropriate or cause discomfort in online communities and communication applications. This research article introduces a novel approach for detecting impolite Thai words, encompassing various Thai dialects, and identifying the regional origin of the offensive words. The new algorithm employs multiple dictionaries; each stored using inverted lists based on character positions rather than direct word storage. This dictionary structure enables the search process to achieve 100% efficiency with no errors. Experimental results from a program developed to detect impolite words in standard Thai, as well as Northern, Southern, and Northeastern Thai dialects, demonstrated 100% accuracy, significantly outperforming previous detection algorithms. Additionally, the new algorithm outperformed current Generative AI models, even without considering sentence context or utilizing AI.

关键词： Computer science Dictionaries Accuracy Generative AI Computational modeling Media Natural language processing detection algorithms Standards Context modeling

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Heuristic Algorithm of Multi-Constraint MHT in Ground-Moving Target Track detection

Heuristic Algorithm of Multi-Constraint MHT in Ground-Moving...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Xiaofu Sun Zhongfei Deng Fei Wang Qinghua Han Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing Univ. Aeronaut. Astronaut.) Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China School of Artificial Intelligence Zaozhuang University Zaozhuang China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The track detection technology of the ultra-wideband (UWB) radar for ground-moving targets is studied in this paper. A heuristic algorithm based on multi-constraint multi-hypothesis tracking (H-MCMHT) is proposed. Some constraints are added to the initial track generation of multi-hypothesis tracking according to the short-time motion characteristics of ground-moving targets. Then a heuristic track association method is proposed. The aim of the algorithm is to obtain as complete a real track as possible in a short time. Simulation results show that compared with MHT, H-MCMHT can effectively reduce the occurrence of false tracks and ensure the detection rate of real tracks. The physical verification results also confirm the simulation conclusions.

关键词： Measurement Target tracking Heuristic algorithms Simulation Radar detection Radar tracking Data processing Computational efficiency detection algorithms Ultra wideband radar

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