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LMP-GAN: Out-Of-Distribution Detection For Non-Control data Malware Attacks

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

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Higher order implicit CNDG-PML algorithm for left-handed materials

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Journal of Systems Engineering and Electronics 2021年第1期32卷 31-37页

作者： CHEN Yanfang WANG Liwei School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing Tianjin Zhongwei Aerospace Data System Technology Co.Ltd.Tianjin 300450China

By incorporating the higher order concept,the piecewise linear recursive convolution(PLRC)method and CrankNicolson Douglas-Gunn(CNDG)algorithm,the unconditionally stable complex frequency shifted nearly perfectly matched layer(CFS-NPML)is proposed to terminate the left-handed material(LHM)*** proposed scheme takes advantages of CFSNPML formulation,the higher order concept PLRC method and the unconditionally stable CNDG algorithm in terms of absorbing performance,computational efficiency,calculation accuracy and convenient implementation.A numerical example is carried out to demonstrate the effectiveness and efficiency of the proposed *** results indicate that the proposed scheme can not only have considerable absorbing performance but also maintain the unconditional stability of the algorithm with the enlargement of time steps.

关键词： complex frequency shifted nearly perfectly matched layer(CFS-NPML) Crank-Nicolson Douglas-Gunn(CNDG) left-handed material(LHM) piecewise linear recursive convolution(PLRC)

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Transmission line inspection system based on high performance unmanned helicopters 4

Transmission line inspection system based on high performanc...

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4th International Conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2024

作者： Li, Wei Li, Ming Meng, Xiangjiao Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing Tianjin Zhong Wei Aerospace Data System Technology Co. Ltd. Tianjin China

ISBN: (数字)9781510679887

ISBN: (纸本)9781510679870

A high-performance unmanned helicopter based transmission line inspection system is proposed to address the issues of low single operation efficiency, low degree of autonomy, and single operation mode in the current multi rotor unmanned aerial vehicle inspection system. This system is based on an unmanned helicopter platform, with customized research and development of load quick replacement and mounting structural components, airborne load control units, optoelectronic pods, autonomous inspection software systems, and other parts. The number of towers that can be inspected in a single fine inspection operation is not less than 20 (visibility, distance between towers is 0.3 km), and the photographic data can meet the requirements of identifying pin level targets (10mm) 50 meters away. Through flight verification testing, it has been shown that the various working modes of the system are operating normally, and autonomous inspections do not require human intervention, greatly improving the efficiency of transmission line inspections. © 2024 SPIE.

关键词： Electric lines

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A Fast Filling Algorithm for Image Restoration Based on Contour Parity

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Computers, Materials & Continua 2020年第4期63卷 509-519页

作者： Yan Liu Wenxin Hu Longzhe Han Maksymyuk Taras Zhiyun Chen School of Data Science and Engineering East China Normal UniversityShanghai200062China JiangXi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of TechnologyNanchang330099China Department of Telecommunication Institute of Telecommunication Radio Electronics and Electronic EngineeringLviv Polytechnic National UniversityLviv79013Ukraine

Filling techniques are often used in the restoration of *** the existing filling technique approaches either have high computational costs or present problems such as filling holes *** paper proposes a novel algorithm for filling holes and regions of the *** proposed algorithm combines the advantages of both the parity-check filling approach and the region-growing inpainting *** points of the region’s boundary are used to search and to fill the *** scanning range of the filling method is within the target *** proposed method does not require additional working memory or assistant colors,and it can correctly fill any complex *** results show that,compared to other approaches,the proposed algorithm fills regions faster and with lower computational cost.

关键词： Region filling image restoration parity check region growing

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A Deep Reinforcement Learning Framework for Vehicle Detection and Pose Estimation in 3D Point Clouds 6th

A Deep Reinforcement Learning Framework for Vehicle Detectio...

引用

6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Wang, Weipeng Luo, Huan Zheng, Quan Wang, Cheng Guo, Wenzhong Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350003 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350003 China Fujian Key Laboratory of Sensing and Computing for Smart City Xiamen University XiamenFJ361005 China

ISBN: (纸本)9783030578800

As for autonomous driving in urban environments, it is of significance to accurately capture the position and pose of vehicles. Those information can assist self-driving system in making right decisions to avoid potential risks. Currently, 3D point clouds captured by laser scanners are widely used in self-driving systems to sense the real environment. Therefore, in this paper, we propose a deep reinforcement learning framework for vehicle detection and pose estimation by using 3D point clouds. Specifically, to estimate the pose of vehicles, we propose to design a rotation action in Deep Q Network (DQN). In addition, by considering the whole detection procedure as Markov Decision Process (MDP), our intermediate detected results can further improve the detection performance of our proposed method. The evaluations are carried on outdoor point cloud scenes captured by VMX450 laser scanning system. The experimental results demonstrate the satisfied performance on vehicle detection and pose estimation. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Study on Pedestrian Evacuation Model Base on Reinforcement Learning

SSRN

引用

SSRN 2024年

作者： Rui, Zhu Hu, Jun Ling, Fan Qi, Zhang Juan, Wei School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Key Laboratory of Interior Layout Optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Chengdu611130 China School of Intelligent Manufacturing Chengdu Technological University Chengdu611730 China Key Laboratory of Multidimensional Data Sensing and Intelligent Information Processing Dazhou Key Laboratory Dazhou635000 China

This paper proposed a pedestrian evacuation model combined with reinforcement learning in order to study how to better guide pedestrians to complete evacuation in specific indoor scenes. This model introduced the way of establishing a scene in cellular automata and formulated reward rules according to the characteristics of the scene. It fitted the psychological activities of pedestrians in the actual evacuation process and trained the strategy of pedestrians at the overall level through the Q-learning algorithm from the reinforcement learning area. A speed control mechanism combined with real statistical data was introduced to simulate the speed attenuation. A simulation platform was built to compare the evacuation conditions under different scenarios and the different total numbers of pedestrians. The research showed that the model could automatically realize the exit selection function of pedestrians and part of conformity behavior. In the same evacuation scenario, this model could show adaptability for the different total numbers of pedestrians. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Visible-Thermal Multiple Object Tracking: Large-scale Video dataset and Progressive Fusion Approach

arXiv

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arXiv 2024年

作者： Zhu, Yabin Wang, Qianwu Li, Chenglong Tang, Jin Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Public Safety and Emergency Management Anhui University of Science and Technology Hefei231131 China School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Center for Big Data and Population Health of IHM China

The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple Object Tracking (MOT). In this work, we contribute a large-scale Visible-Thermal video benchmark for MOT, called VT-MOT. VT-MOT has the following main advantages. 1) The data is large scale and high diversity. VT-MOT includes 582 video sequence pairs, 401k frame pairs from surveillance, drone, and handheld platforms. 2) The cross-modal alignment is highly accurate. We invite several professionals to perform both spatial and temporal alignment frame by frame. 3) The annotation is dense and high-quality. VT-MOT has 3.99 million annotation boxes annotated and double-checked by professionals, including heavy occlusion and object re-acquisition (object disappear and reappear) challenges. To provide a strong baseline, we design a simple yet effective tracking framework, which effectively fuses temporal information and complementary information of two modalities in a progressive manner, for robust visible-thermal MOT. A comprehensive experiment are conducted on VT-MOT and the results prove the superiority and effectiveness of the proposed method compared with state-of-the-art methods. From the evaluation results and analysis, we specify several potential future directions for visible-thermal MOT. The project is released in https://***/wqw123wqw/PFTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Hyperspectral Image Fusion via Logarithmic Low-rank Tensor Ring Decomposition

arXiv

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arXiv 2023年

作者： Zhang, Jun Zhu, Lipeng Wang, Chao Li, Shutao College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China College of Science Nanchang Institute of Technology Jiangxi Nanchang330099 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Jiangxi Nanchang330099 China Department of Statistics and Data Science Southern University of Science and Technology Guangdong Shenzhen518055 China

Integrating a low-spatial-resolution hyperspectral image (LR-HSI) with a high-spatial-resolution multispectral image (HR-MSI) is recognized as a valid method for acquiring HR-HSI. Among the current fusion approaches, the tensor ring (TR) decomposition-based method has received growing attention owing to its superior performance on preserving the spatial-spectral correlation. Furthermore, the low-rank property in some TR factors has been exploited via the matrix nuclear norm regularization along mode-2. On the other hand, the tensor nuclear norm (TNN)-based approaches have recently demonstrated to be more efficient on keeping high-dimensional low-rank structures in tensor recovery. Here, we study the low-rankness of TR factors from the TNN perspective and consider the mode-2 logarithmic TNN (LTNN) on each TR factor. A novel fusion model is proposed by incorporating this LTNN regularization and the weighted total variation which is to promote the continuity of HR-HSI in the spatial-spectral domain. Meanwhile, we have devised a highly efficient proximal alternating minimization algorithm to solve the proposed model. The experimental results indicate that our method improves the visual quality and exceeds the existing state-of-the-art fusion approaches with respect to various quantitative metrics. © 2023, CC BY.

关键词： Tensors

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