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Local-GAN: An Anomaly Detection Method based on Local Key Features and GAN for IoT 17

Local-GAN: An Anomaly Detection Method Based on Local Key Fe...

引用

IEEE Congress on Cybermatics: 17th IEEE international conference on internet of Things, iThings 2024, 20th IEEE international conference on Green Computing and Communications, GreenCom 2024, 17th IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE international conference on Smart Data, SmartData 2024

作者： Lai, Huixia Zhou, Fan Wang, Bo Chen, Hongrui Zhang, Shi Fujian Normal University College of Computer and Cyber Security Fuzhou China Fujian Meteorological Information Centre Fuzhou China

ISBN: (纸本)9798350351637

In internet of Things (IoT) anomaly detection (AD) tasks, reconstruction-based AD methods are widely concerned due to their simple network structure and ease of training. However, they tend to directly use the original image as the output due to their powerful generalization ability. To overcome this, we propose a novel reconstruction-based anomaly detection method based on local key features and generative adversarial network (named Local-GAN) for IoT. Firstly, Local-GAN utilizes the dual feature encoders to extract local features, and select key features with masks generated by a mask generator. based on the selected local key features, the decoder avoids reconstructing the anomaly sample/area as it is, which improves the performance. Secondly, the quality-enhanced discriminator is imported into the model to improve the quality of reconstructed images, making it easier to distinguish anomalies. The experiment results on 3 image datasets and 1 real-world industrial image dataset show that our method outperforms reconstruction-based anomaly detection methods in image AD tasks. Furthermore, the experiment results on 13 structured datasets demonstrate the generality of our method. © 2024 IEEE.

关键词： Gallium nitride

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Research on image Encryption Algorithm based on Chaotic System 4

Research on Image Encryption Algorithm Based on Chaotic Syst...

引用

4th IEEE international conference on Data Science and Computer Application, ICDSCA 2024

作者： Yihang, Wu Yanling, Sun Yingqi, Li School of Mechanical Engineering Shenyang Jianzhu University Shenyang China Department of Science Shenyang Jianzhu University Shenyang China Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798350368239

With the development of the internet, social software and media have become ubiquitous, making images increasingly important in daily life. The dissemination of a large number of images on social networks has made information security issues increasingly prominent, and image encryption technology has become increasingly important. This article mainly studies image encryption algorithms based on chaotic systems. Firstly, the background and history of image encryption were introduced, followed by an explanation of the basic concepts based on chaotic systems. Then, several chaos based image encryption schemes were comprehensively compared, and their advantages and disadvantages were analyzed. Subsequently, several common measures for evaluating the effectiveness of encryption schemes were introduced. Finally, the future development of image encryption algorithms based on chaotic systems is discussed. ©2024 IEEE.

关键词： Encryption algorithms

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Fault Diagnosis Method of Servo Motor Bearing Installation Misalignment based on CSFF-CNN 27

Fault Diagnosis Method of Servo Motor Bearing Installation M...

引用

27th international conference on Electrical Machines and systems, ICEMS 2024

作者： Wang, Jing Li, Jianye Yang, Ming Zhang, Xinmei School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Ningbo Anson Cnc Technology Co. Ltd Ningbo China

ISBN: (纸本)9784886864406

As an important part of servo motor, bearing misalignment fault diagnosis has attracted more and more attention. In order to improve the fault diagnosis performance of servo motor gear, a fault diagnosis method based on compressed sensing feature fusion is proposed. In this paper, the alpha phase current is collected and converted into the image by the image transformation method. In order to effectively improve the feature extraction ability of the model for key information, this paper proposes to use the compressed sensing of the signal and the compressed sensing of the signal transformed into the image and the neural network to extract the feature of the original signal transformed into the image. In order to improve the feature extraction ability of the model for multi-gradient information flow, this paper proposed a module named multi-gradient information flow module. The performance of the model proposed in this paper has been verified by the collected servo motor gear data. Under different working conditions, the effect of the method proposed in this paper is improved compared with that of convolution neural network, and the model has strong generalization ability, which proves the superiority of the model performance. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Compressed sensing

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Localized Binarization of Document images based on Suprathreshold Stochastic Resonance 4

Localized Binarization of Document Images Based on Suprathre...

引用

4th international conference on Electronic Information Engineering and Computer technology, EIECT 2024

作者： Yan, Xiaoyue Mu, Dazhong School of Information Engineering Beijing Institute Of Graphic Communication Beijing China

ISBN: (纸本)9798331528850

When processing text images with traditional binarization methods, the image background noise often causes the results to become blurred or leads to the loss of edge details. To solve this problem, this paper proposes an image binarization method based on stochastic resonance theory. First, we divide the image into sub-blocks and set a binarization threshold based on the statistical properties of the pixels in each sub-block. Next, the image signal is converted into a one-dimensional time series signal using Hilbert scanning. The processed signals are input into a threshold array system, which amplifies the weak edge information in the input signals through the stochastic resonance phenomenon. Subsequently, we performed modulation and inverse scanning on the output signals of the system to generate the binary image for each sub-block. Finally, all sub-block binary images were combined to complete the binarization of the overall image. Experimental results show that the method proposed in this paper can effectively retain the detailed information of document images and significantly outperforms the traditional binarization method regarding image quality. © 2024 IEEE.

关键词： Stochastic systems

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Approach for Double Security with Watermarking and Chaos based Encryption for e-Healthcare systems 4

Approach for Double Security with Watermarking and Chaos Bas...

引用

4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Singh, Harpreet Sant Longowal Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Sangrur148106 India Sant Longowal Institute of Engineering and Technology Department of Electrical and Instrumentation Engineering Punjab Sangrur148106 India

ISBN: (纸本)9798350395914

Medical data of the patients includes sensitive information and needs security while transmit over the internet. The double security approach based on watermarking and encryption method has been purposed in this work to secure the patient information consisting personal information, clinical information, and diagnostic records along with the medical images. The watermarking method is introduced on the basis of the least significant bit (LSB) method that conceals the patient information in the LSB bit of the image to hide the information. As, the LSB bit of the image is consider as least informative bit. Thus, it reduces the loss of data in the image. Then, the watermarked image is secured using the hybrid encryption method based on the Self Adaptive Bald Eagle Search (SABES) optimization algorithm, multiple chaotic maps, and circular shift. Chaotic maps are employed in the proposed approach to increase randomness in the encrypted image and chaotic maps' parameters are optimized employing the SABES optimization method to increase the chaotic maps efficiency. The propounded approach is tested on different types of medical images i.e., Grayscale, RGB, and hyperspectral images having different modalities and sizes. The performance of the propounded approach has been analyzed utilizing the Normalized Correlation (NC), Peak signal-to-Noise Ratio (PSNR), entropy, histogram, Number of Changing Pixel Rate (NPCR), Structural Similarity Measure (SSIM), and Unified Averaged Changed Intensity (UACI) test. The proposed approach performance shows that the medical information secure from different types of attacks. © 2024 IEEE.

关键词： Electronic health record

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Background estimation under mobile platform based on dynamic template phase matching 4

Background estimation under mobile platform based on dynamic...

引用

4th international conference on Laser, Optics, and Optoelectronic technology, LOPET 2024

作者： Liang, Zichun Xu, Yinghui Qian, Weixian School of Electronic and Optical Engineering Nanjing University of Science and Technology Jiangsu province Nanjing210094 China

ISBN: (纸本)9781510681903

Background estimation and motion target detection under moving platform are key technologies in motion camera system, which has attracted much attention in recent years and is widely used in security monitoring, UAV detection and other optoelectronic detection systems. When the camera is mounted on a moving platform, the movement of the platform causes a corresponding displacement of the image background captured by the camera. Therefore, the stationary background estimation method based on a stationary camera is no longer applicable in this case. In this paper, we propose a method for calculating the image motion information using the dynamic template phase matching method. Homologous signal trajectories are then established based on the image motion information to compensate for the inverse motion of the background image. Finally, a more accurate stationary background image is obtained using the background modeling method. © 2024 SPIE.

关键词： Motion capture

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Point Cloud Quality Assessment using 1D VGG16 based Transfer Learning Model 17

Point Cloud Quality Assessment using 1D VGG16 based Transfer...

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17th international conference on signal-image technology and internet-based systems, SITIS 2023

作者： Laazoufi, Abdelouahed Hassouni, Mohammed El Cherifi, Hocine Mohammed v University in Rabat Rabat Morocco Mohamed v University in Rabat Flsh Rabat Morocco University of Burgundy France

ISBN: (纸本)9798350370911

This paper introduces a novel approach for assessing the quality of 3D point clouds using a deep learning-based no-reference (NR) method. Our framework employs transfer learning to extract high-level features from distorted point clouds. We incorporate the VGG16 model as the foundation for our modified trained model. To achieve this, we make slight modifications to the VGG16 model, transforming them into 1D variants. Additionally, we leverage the weights from imageNet-trained 2D models to obtain weights for our 1D model. By utilizing this model, we extract both geometric and perceptual features. The point cloud characteristics are estimated using statistical properties such as entropy, standard deviation, skewness, kurtosis, median, and mean. We employ the Support Vector Regressor (SVR) for regression and quality score prediction. To validate our method, we conduct experiments on subjective databases, including (SJTU-PCQA, and ICIP2020). The results demonstrate that the proposed method outperforms several competing methods. © 2023 IEEE.

关键词： Deep learning

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A Data Leakage Traceability Scheme based on Differential Privacy and Fingerprint 3

A Data Leakage Traceability Scheme Based on Differential Pri...

引用

3rd international conference on image Processing and Media Computing (ICIPMC)

作者： Wang, Mingyong Zheng, Shuli Hefei Univ Technol Sch Comp & Informat Sci Hefei Peoples R China

ISBN: (纸本)9798350386660;9798350386677

With the development of the internet and big data technology, massive amounts of data are transferred between different application systems and organizations, greatly increasing the difficulty of privacy protection and the risk of data leakage. Existing database fingerprinting schemes, while addressing the problem of traceability, are limited by the contradiction between the robustness of the fingerprints and the data distortion introduced by the embedding of fingerprints. They also struggle to protect sensitive data while maintaining data usability. To address such issues, this paper proposes a data leakage traceability scheme based on differential privacy and fingerprinting. This scheme processes the database using the Laplace mechanism for differential privacy, then treats the fingerprint information with the Arnold image transformation algorithm, and trains an approximate optimal key based on the fingerprint information using a genetic algorithm. This approach aims to enhance the robustness of the fingerprint scheme while reducing data distortion. Experimental results prove that this scheme has better robustness than existing fingerprint schemes after maintaining usability.

关键词： database fingerprinting genetic algorithm differential privacy Arnold image transformation

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Accurate azimuth estimation method for FMCW radar based on moving arrays

Accurate azimuth estimation method for FMCW radar based on m...

引用

2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： An, Tai Yan, Xiaopeng Han, Yanwen Dai, Jian Wang, Jiawei Science and Technology on Electromechanical Dynamic Control Laboratory School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510680425

Addressing the current challenges of weak target azimuth detection and inadequate precision in frequency modulated continuous wave (FMCW) radar systems, this paper proposes a novel method for precise imaging based on a motion array using FMCW radar for accurate range and azimuth estimation. The approach involves the use of a millimeter-wave FMCW radar with a motion array to capture target angular information, leading to a substantial improvement in azimuth estimation resolution, particularly for munitions. Additionally, spatial Fourier transform array signal processing is employed to achieve two-dimensional precise imaging of radar targets, encompassing both range and azimuth dimensions. Experimental results validate the effectiveness of the proposed approach, showcasing an enhanced angular resolution of the millimeter-wave radar, reaching 2.6°. The method demonstrates favorable azimuth estimation outcomes in diverse scenarios, including those involving multiple and planar targets. Overall, the introduced technique significantly elevates the precise detection capability of automotive millimeter-wave radar systems. © 2024 SPIE.

关键词： Radar imaging

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internet of Things Anomaly Detection Enhancement using GAN-based Data Augmentation 17

Internet of Things Anomaly Detection Enhancement using GAN-b...

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17th international conference on signal Processing and Communication systems, ICSPCS 2024

作者： Wong, Christopher Wang, Min Hossain, Md Farhad Munasinghe, Kumudu University of Canberra Faculty of Science and Technology ACT2617 Australia

ISBN: (纸本)9798350389630

Anomaly detection in internet of Things (IoT) systems is essential for preventing failures, security breaches, and inaccurate predictions. Deep learning-based approaches are effective for this purpose but often suffer from insufficient labeled training data, which limits detection accuracy. To address this challenge, we propose a novel anomaly detection framework that embeds synthetic data generation with the anomaly detection process. The framework integrates a Generative Adversarial Network (GAN)-based module that learns underlying patterns in normal and/or abnormal data to generate synthetic samples, effectively augmenting the training set. This enriched dataset allows the anomaly detection module to better capture data variability and improve accuracy. For anomaly detection, raw data is first converted into spectrogram images to enhance feature representation, which are then encoded by a pre-trained Convolutional Neural Network (CNN) for effective feature extraction. These features are passed to one of two detection models: a deep Autoencoder, which learns representations of normal data in an unsupervised manner and identifies anomalies by reconstruction error, or a CNN classifier, which uses supervised learning to classify normal and abnormal data. Experimental results show that GAN-based data augmentation significantly boosts detection performance in both the supervised and unsupervised learning cases, making this approach highly effective for reliable anomaly detection in IoT systems. © 2024 IEEE.

关键词： Generative adversarial networks

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