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23rd IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Dargaud, Laurine Ibsen, Mathias Tapia, Juan Busch, Christoph Tech Univ Denmark DTU Lyngby Denmark Hsch Darmstadt Hda Da Sec Biometr & Internet Secur Res Grp Darmstadt Germany

ISBN: (纸本)9798350320565

This paper proposes an explicit method for single face image morphing attack detection, using an RGB decomposition based on Principal Component Analysis from texture patterns. Handcrafted detection algorithms can be advantageous over deep learning-based methods as they constitute increased explainability, showcased in this work by visualizing relevant face areas for morphing attack detection. Such information can be relevant for deployed systems in real-world scenarios with humans in the loop. The morphing detection capability of the proposed method is evaluated extensively across three datasets and six morphing algorithms in single, cross-dataset and cross-morphed scenarios and compared to a fine-tuned MobileNetV2 architecture. The results show how single image morphing attack detection remains challenging, especially in cross-domain scenarios involving realistic diversity of morphing algorithms, including StyleGAN-based approaches. In such conditions, the proposed method can be as good or even better than the evaluated MobileNetV2 approach.

关键词： Training Learning systems Visualization Image color analysis Feature extraction Human in the loop detection algorithms

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Compressive Sensing-Based Joint Activity and Data detection for Grant-Free Massive IoT Access

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第3期21卷 1851-1869页

作者： Mei, Yikun Gao, Zhen Wu, Yongpeng Chen, Wei Zhang, Jun Ng, Derrick Wing Kwan Di Renzo, Marco Beijing Inst Technol Sch Informat & Elect Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2025 Australia Univ Paris Saclay Lab Signaux & Syst CNRS Cent SupelecUniv Paris Sud F-91192 Paris France

Massive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internet-of-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulate the JADD as a multiple measurement vectors (MMV) compressive sensing problem. Moreover, to leverage the structured sparsity of uplink massive access signals among multiple time slots, we develop two computationally efficient detection algorithms, which are termed as orthogonal approximate message passing (OAMP)-MMV algorithm with simplified structure learning (SSL) and accurate structure learning (ASL). To achieve accurate detection, the expectation maximization algorithm is exploited for learning the sparsity ratio and the noise variance. To further improve the detection performance, channel coding is applied and successive interference cancellation (SIC)-based OAMP-MMV-SSL and OAMP-MMV-ASL algorithms are developed, where the likelihood ratio obtained in the soft-decision can be exploited for refining the activity identification. Finally, the state evolution of the proposed OAMP-MMV-SSL and OAMP-MMV-ASL algorithms is derived to predict the performance theoretically. Simulation results verify that the proposed solutions outperform various state-of-the-art baseline schemes, enabling low-latency random access and high-reliable massive IoT connectivity with overloading.

关键词： Inference algorithms Approximation algorithms Uplink OFDM Performance evaluation Wireless communication detection algorithms Compressive sensing grant-free massive access orthogonal approximate message passing multiple measurement vectors successive interference cancellation

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DC Series Arc Fault Detector With Differential DWT and Variable Threshold Method for Photovoltaic Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第7期20卷 9343-9351页

作者： Ahn, Jae-Beom Jeong, Seung-Jae Cho, Chan-Gi Ryoo, Hong-Je Chung Ang Univ Dept Energy Syst Engn Seoul 06974 South Korea Agcy Def Dev Dept Laser Weap Syst PMO Daejeon 34186 South Korea Chung Ang Univ Sch Energy Syst Engn Seoul 06974 South Korea

A series arc fault detector (AFD) is a significant device for preventing fire hazards in photovoltaic (PV) systems. The AFD should detect a series arc quickly and accurately. However, system noise due to the components of a PV system can cause false detection of the AFD. Furthermore, as the inverter types vary according to PV systems and the irradiation changes during one day, it is difficult to develop a universal arc fault detection algorithm with an adequate arc detection criterion, and these difficulties limit the commercialization of AFDs. This study proposes an arc detection algorithm based on differential discrete wavelet transform (DWT) analysis and variable threshold method. Differential DWT analysis increases the distinction performance of inverter noise and arc noise and detects series arcs quickly and effectively by acquiring the amplified arc noise and attenuated inverter noise. Moreover, the variable threshold method updates the adequate threshold level for arc detection in real time and does not require manual tuning for systems and irradiation change. This study also proposes an AFD based on the TMS320F28335 digital signal processor to analyze noise and detect the series arc fault in real time using the proposed algorithm. The performance of the series AFD was verified via a series arc-detection test under the UL1699B test facility. Furthermore, the reliability of the arc detection algorithm and AFD was verified via the unwanted tripping test and arc detection under a real PV system.

关键词： Discrete wavelet transforms Noise Inverters Radiation effects detection algorithms History Real-time systems Arc fault detector (AFD) discrete wavelet transform (DWT) fault diagnosis photovoltaic (PV) system series arc

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A Fast Community detection Algorithm Based on Reconstructing Signed Networks

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IEEE SYSTEMS JOURNAL 2022年第1期16卷 614-625页

作者： Xia, Chengyi Luo, Yongping Wang, Li Li, Hui-Jia Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Engn Res Ctr Learning Based Intelligent Syst Minist Educ Tianjin 300384 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China

Signed networks depict the individual cooperative or hostile relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or communities. In this article, first of all, an improved modularity function for signed networks is proposed on the basis of the existing modularity function. Then, a new community detection algorithm for signed networks has also been devised, and time complexity analysis shows that the time required for the algorithm has a linear relationship with the number of nodes in the sparse networks. Meanwhile, the affinity index that can be used to convert directed signed networks into the corresponding undirected signed networks is come up with. Finally, the current algorithm has been applied into several illustrative and realistic networks. The experimental results indicate that the number of communities given by the proposed algorithm is consistent with that of actual communities, and thus, it can be further conducive to identifying the community structure hidden within the real-world systems.

关键词： Indexes Partitioning algorithms Image edge detection Clustering algorithms Oscillators Heuristic algorithms detection algorithms Affinity index community detection community structure modularity network reconstruction signed network

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A Signature Recognition Technique With a Powerful Verification Mechanism Based on CNN and PCA

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IEEE ACCESS 2024年 12卷 40634-40656页

作者： Abosamra, Gibrael Oqaibi, Hadi King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Informat Syst Jeddah 21589 Saudi Arabia

In this paper, we embed a signature verification mechanism in a previously introduced architecture for signature recognition to detect in-distribution and out-of-distribution random forgeries. In the previous architecture, a CNN was trained on the genuine user training dataset and then used as a feature extraction module. A k-NN algorithm with cosine distance was then used to classify the unknown signatures based on the nearest cosine distance neighbor. This architecture led to higher than 99% accuracy, but without verification, because any unknown signature will converge to one of the identities of the training dataset's users. To add a verification mechanism that differentiates between genuine and random forgeries, we use PCA to select the most discriminating features used in calculating the cosine distance between the training and testing signatures. A fixed parameter thresholding technique based on the training distances is introduced that best differentiates between the genuine and random-user signatures. Moreover, enhancement of the technique is carried out by combining the output of the Softmax layer and the last convolution layer of the ResNet18 model to get a highly discriminative representation of the handwritten signatures. Accordingly, the introduced verification mechanism resulted in very low false positive and negative rates for test signatures from inside and outside the main dataset, with an insignificant decrease in the high identification accuracy. The complete architecture has been tested on three publicly available datasets, showing superior results.

关键词： Training Principal component analysis Feature extraction Forgery Vectors Convolutional neural networks Data models Nearest neighbor methods detection algorithms Handwriting recognition cosine distance false negative rate false positive rate k-Nearest neighbor algorithm out-of-distribution detection signature identification signature verification

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HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention

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IEEE ACCESS 2024年 12卷 25512-25524页

作者： Zhao, Qin Miao, Yaru An, Dongdong Lian, Jie Li, Maozhen Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Big Data Shanghai 200234 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China Brunel Univ London Dept Elect & Comp Engn London UB8 3PH England

Heterogeneous information networks provide abundant structural and semantic information. Two main strategies for leveraging this data include meta-path-based and meta-path-free methods. The effectiveness of the former heavily depends on the quality of manually defined meta-paths, which may lead to the instability of the model. However, the existing meta-path-free methods lack of neighbor screening during aggregating, and there is also an overemphasis on attribute information. To address these issues, we propose the Heterogeneous Graph Neural Network model by incorporating Quantitative Sampling and Structure-aware Attention. We introduce a Quantitative Sampling Module that calculates the similarity between neighbors of the target nodes and target nodes, enabling us to select the top k nodes with the strongest relevance to the target node based on this measure, and incorporate a Structure-aware Attention Module during the aggregation of neighbor information. This module combines both structural and attribute information to aggregate the neighbor information effectively. By implementing these improvements, our proposed model exhibits superior performance compared to several state-of-the-art methods on two real-world datasets.

关键词： Graph neural networks Social networking (online) Correlation Prediction algorithms Task analysis Stacking Semantics Information systems Heterogeneous networks detection algorithms Heterogeneous information network community detection quantitative sampling structure-aware attention

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A Comprehensive Analysis of the Machine Learning algorithms in IoT IDS Systems

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IEEE ACCESS 2024年 12卷 46785-46811页

作者： Ozdogan, Erdal Gazi Univ IT Dept TR-06560 Ankara Turkiye

In this study, machine learning algorithms in IoT IDS (Internet of Things Intrusion detection System) systems are comprehensively compared from various aspects. Accuracy, precision, and training time are evaluated. The effects of data preprocessing techniques including normalization, outlier removal, standardization, and regularization on the datasets are examined. Furthermore, the impact of dataset balancing, considering both balanced and imbalanced scenarios, on machine learning performance is investigated. The contribution of feature selection on the four different datasets is also analyzed. Based on findings, it is observed that certain preprocessing operations provide significant advantages in various ML algorithms, whereas others have very low impact, and their performance varies depending on the dataset and feature selection. The aim of this study is to facilitate the complexity and lengthiness of machine learning processes and algorithm selection, providing insights for future academic research. By addressing this objective, an effort is made to shed light on simplifying the utilization of machine learning algorithms. The challenges arising from the complexity of machine learning processes in IoT IDS systems are addressed by this study. This contribution can greatly benefit researchers in their academic endeavors. This multifaceted approach proves beneficial when comparing the methods under consideration, fostering a scientific discourse on their efficacy within contexts.

关键词： detection algorithms IoT attack detection IoT IDS ML algorithm comparison machine learning preprocessing machine learning in IoT IDS

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Infrared Small UAV Target detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks

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IEEE ACCESS 2022年 10卷 115988-115995页

作者： Zheng Lu Peng Yueping Ye Zecong Jiang Rongqi Zhou Tongtong Engn Univ PAP Xian 710086 Peoples R China Engn Univ PAP Coll Informat Engn Xian 710086 Peoples R China

Under the explosive growth of "low-slow-small" unmanned aerial vehicles (UAVs), harassment incidents with "low-slow-small" UAVs, such as illegal surveying and mapping, no-fly zone flights, terrorist attacks, have frequently been occurring due to the low entry barrier to the industry and the lack of market norms and control standards, and severely impair the safety of public life and property. The premise of developing anti-UAV technology is efficient and accurate UAV target detection, which plays a vital role in the follow-up UAV target tracking, attack, and interception. This paper proposes a new DEAX algorithm named enhanced adaptive feature pyramid networks-based Target detection Algorithm for infrared small UAV target detection. Our proposed algorithm improves the original feature pyramid networks in four aspects for the small UAV target detection task. 1) Use channel separation to keep more channels when adjusting the number of channels in convolutional to avoid the loss of helpful feature information;2) Design a feature enhancement module to enhance "target feature" and suppress "non-target feature";3) Alleviate the differences of receptive fields and semantic information among different layers by shared convolution;4) Introduce adaptive feature fusion method into feature fusion and complete the construction of enhanced adaptive feature pyramid networks (EAFPN) to solve the problem of weakening feature expression in cross-scale feature fusion. EAFPN is applied to the single-stage target detection networks. We find the detection accuracy and speed of the algorithm outperform those based on feature pyramid networks (FPN), where the improvement on mAP is 8.9%.

关键词： Feature extraction Object detection Semantics detection algorithms Target tracking Imaging Classification algorithms Object detection feature pyramid feature enhancement

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Label Propagation Algorithm Joint Multilayer Neighborhood Overlap and Historic Label Similarity for Community detection

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 2626-2634页

作者： Zhang, Siyang Yu, Siyu E, Xinhua Huo, Ru Sui, Ziheng Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Peking Univ Fac Informat Technol Sch Software & Microelect Beijing 102600 Peoples R China

Community detection is an extremely important technology for today's rapidly evolving data mining and exploratory analysis. Many community detection algorithms have been proposed and applied. Among them, the label propagation algorithm (LPA) is widely used due to its closeness to linear time complexity and its simple characteristics. However, the results of detecting the community were found to be random when using LPA. Based on previous improvements to the LPA, this article proposes a new LPA (NOHLPA) that joins multilayer neighborhood overlap and historical label similarity. The NOHLPA considers both the node update order and label selection rules. We cited the label entropy as the basis for node update order and defined multilayer neighborhood overlap and historical label similarity for calculating node preferences to devise more appropriate label selection rules. We test the NOHLPA on five real datasets and compare NOHLPA with different combinations of algorithms. The experimental results show that the NOHLPA effectively improves the accuracy of community partitioning while ensuring stability.

关键词： Entropy Nonhomogeneous media Internet Time complexity Social networking (online) Heuristic algorithms detection algorithms Community detection historical label similarity label propagation multilayer neighborhood overlap

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A Methodology for Forest Fire detection and Notification Using AI and IoT Approaches 2

A Methodology for Forest Fire Detection and Notification Usi...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Borah, Asha Rani Ravi, Nikhil Narayan, Nikhil D'Souza, Prathik Joel New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This study explores how advanced technology has revolutionized the use of drones for forest fire surveillance. This research study explores the historical development of Unmanned Aerial Vehicles (UAVs) in forest monitoring with a focus on control systems and detection algorithms. The drone's capabilities are improved via telemetry integration, sophisticated autopilot systems, and an emphasis on accuracy, autonomy, and real-Time communication. An analysis of object recognition, anomaly detection, and environmental monitoring algorithms in detail demonstrates how important a role these algorithms play in spotting dangers like forest fire. This study presents successful implementations through illustrative case studies, discusses obstacles, and lays forth future plans for utilizing drone technology for the forest fire surveillance in environmental conservation initiatives. © 2024 IEEE.

关键词： Autonomy Control systems Cutting-edge technology detection algorithms Forest surveillance drones Historical evolution Real-Time communication Unmanned Aerial Vehicles

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