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检索条件"任意字段=International Conference on Signal Image Technology and Internet Based Systems"

共 7677 条记录，以下是91-100 订阅

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MACHINE-ATTENTION-based VIDEO CODING FOR MACHINES 30

MACHINE-ATTENTION-BASED VIDEO CODING FOR MACHINES

引用

30th IEEE international conference on image Processing (ICIP)

作者： Lee, Yegi Kim, Shin Yoon, Kyoungro Lim, Hanshin Kwak, Sangwoon Choo, Hyon-Gon Konkuk Univ Seoul South Korea Elect & Telecommun Res Inst ETRI Daejeon South Korea

ISBN: (纸本)9781728198354

Conventional video coding methods have been developed based on the human visual system (HVS). However, in recent years, video has occupied a huge portion of internet traffic, and the mount of video data for machine consumption has increased rapidly due to the progress of neural networks. This paper proposes a novel machine-attention-based video coding method for machines. Inspired by the saliency-driven research, we first extract attention regions, sensitively affecting the machine vision performance, from the object detection network. Subsequently, a maximum a posterior (MAP)-based bit allocation method is applied to assign more bits to the attention regions. Our proposed method helps to maintain high machine vision performance whereas reducing the bitrate. Experimental results show that our proposed method achieves up to 34.89% bjontegaard delta (BD)-rate reduction for the video dataset and up to 44.70% BD-rate reduction for the image dataset compared to state-of-the-art video coding technology.

关键词： Video Coding for Machines Video Compression image Compression Machine-Attention MPEG VCM

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Attack detection on multi-architecture internet of Vehicles honeypots and response mechanism research 24

Attack detection on multi-architecture Internet of Vehicles ...

引用

3rd international conference on signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Ning, Yuqiao Huo, Quanrui Mu, Shiyu Xue, Shihao China Automobile Data of Tianjin Co. Ltd. Tianjin China CATARC Intelligent and Connected Technology Co. Ltd. Tianjin China

ISBN: (纸本)9798400710834

Intelligent connected vehicles (ICVs) refer to a new vehicle type that utilizes advanced information and communication technology to enable information exchange among vehicles, between vehicles and roads, and between vehicles and the internet. Malicious actors may exploit vulnerabilities in ICVs to gain malicious control, steal data, or launch denial-of-service attacks, resulting in significant economic losses and personal harm. Therefore, it is crucial to research attack detection and response mechanisms for ICVs and build effective network defense systems to ensure the safe operation of these vehicles. However, different manufacturers may adopt different architectures, posing significant challenges to network security defense for these vehicles. To address this issue, this paper proposes an attack detection and response mechanism based on Docker container technology for Multi-Architecture Vehicle Honeypots (MAV-honeypots). The aim is to simulate ICVs under various architectures using honeypot technology, attracting and inducing attackers to carry out attacks, thus achieving identification, analysis, and response to attackers. © 2024 Copyright held by the owner/author(s).

关键词： Network security

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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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Leveraging Enhanced Manta-Ray Foraging Optimization with Deep Learning for Maize Diseases image Classification 17

Leveraging Enhanced Manta-Ray Foraging Optimization with Dee...

引用

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

作者： Mostafa, Reham R. Marie, Attiea El-Bannany, Magdi Khedr, Ahmed M. University of Sharjah Research Inst. of Sci. and Eng. Sharjah27272 United Arab Emirates Umm Al Quwain University College of Business Administration Umm Al Quwain536 United Arab Emirates University of Sharjah Computer Science Dept. Sharjah27272 United Arab Emirates

ISBN: (纸本)9798350370911

The manual identification of maize diseases is labor-intensive, requiring expertise and incurring substantial costs. Consequently, there is a pressing need for computer-based automated systems. Maize diseases not only compromise quality but also quantity. Addressing this, early disease detection and subsequent remedies can be facilitated through computer-based techniques. Nevertheless, this approach encounters challenges, including suboptimal contrast, limited training datasets, and inadequacies in feature extraction for final classification. This paper introduces an automated framework for maize leaf disease detection, harnessing CNN pre-trained models in conjunction with an Enhanced Manta-Ray Foraging Optimization (EMRFO) algorithm. Initial data augmentation is executed to harmonize the chosen maize dataset, followed by the fine-tuning and training of two pre-trained deep models through transfer learning. A fusion technique is then applied to consolidate feature vectors, subsequently optimized using the EMRFO algorithm. The resultant selected features undergo classification via the K-Nearest Neighbors (KNN) algorithm. Experimental validation is conducted on the non-augmented Plant Village dataset, yielding an exceptional accuracy of 98.7%. Furthermore, the performance of the proposed framework is compared with other CNN models, emerging as the superior performer. © 2023 IEEE.

关键词： Classification (of information)

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Numerical simulation and analysis of leakage signal characteristics of different leakage holes in gas pipelines 3

Numerical simulation and analysis of leakage signal characte...

引用

3rd international conference on Advanced Manufacturing technology and Manufacturing systems, ICAMTMS 2024

作者： Han, Yu Xiao, Junsheng Zuo, Hongfei Zhou, Xiaofeng School of Automation and Electrical Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou014010 China Department of Electrical Engineering Baotou Vocational and Technical College Inner Mongolia Baotou014030 China

ISBN: (纸本)9781510681804

In this study, numerical simulation and analysis of leakage signal characteristics of gas pipelines with different leakage holes were carried out based on Fluent software. It was found that the differences in leakage holes significantly affect the flow field distribution of the leaking pipeline, which in turn affects the leakage detection results. The leakage hole structures and dynamic characteristics of the model significantly affect the acoustic response, and the model with a ball valve produces a higher sound pressure level. The experimental results provide guidance for the design of piping systems and the detection of leaks, and are of great significance in analysing the differences in flow field and noise generation of different leakage hole models. They provide a scientific basis for the design of piping systems and the detection of leaks. © 2024 SPIE.

关键词： Gas pipelines

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Gait Feature Perception and Identity Recognition Under Visual-Tactile Dual Modality 9

Gait Feature Perception and Identity Recognition Under Visua...

引用

9th international conference on Communication, image and signal Processing, CCISP 2024

作者： Yang, Huiyu Song, Fengqian Zhang, Yangyang DataGrand Co. LTD. Shanghai China Internet Academy Anhui University Hefei China

ISBN: (纸本)9798350356656

Gait recognition technology, as a key branch in the field of identity recognition, has significant value but also faces challenges such as discomfort from wearable devices, the need for synchronized processing of gait feature time series data, and susceptibility to environmental influences in image-based methods. A fusion method combining tactile and visual information is proposed to improve recognition effects when sensors or visual information are limited. Not only is a fusion gait dataset constructed, but identity recognition experiments are also conducted on flexible strain sensor data through Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) CNN-LSTM networks. The effectiveness of the visual-tactile fusion method in enhancing recognition accuracy is verified through decision-level fusion strategies. © 2024 IEEE.

关键词： Convolutional neural networks

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FG-PIH: A Fusion of Fresnelet Transform and Gradient Directional Pattern for Perceptual image Hashing 2

FG-PIH: A Fusion of Fresnelet Transform and Gradient Directi...

引用

2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Meesala, Pavani Thounaojam, Dalton Meitei Computer Science and Engineering National Institute of Technology Silchar Computer Vision Laboratory Silchar India

ISBN: (纸本)9798350366570

Perceptual image hashing is pivotal in various image processing applications, including image authentication, content-based image retrieval, tampered image detection, and copyright protection. This paper proposes a novel approach for perceptual image hashing by combining the Fresnelet Transform with Gradient Directional Patterns. Using the FG-PIH technique, the proposed method achieves superior robustness against common image processing attacks while maintaining perceptual similarity for near-duplicate images. Experimental results on standard benchmark datasets demonstrate the effectiveness and efficiency of the proposed Fresnelet Transform-based perceptual image hashing scheme. Furthermore, comparative analysis against state-of-the-art methods underscores the competitiveness of our approach in terms of hash quality and computational complexity. © 2024 IEEE.

关键词： Hamming distance

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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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An Automated Hybrid Fusion Network for Poisson Noise Reduction in X-Ray Osteoarthritis images

An Automated Hybrid Fusion Network for Poisson Noise Reducti...

引用

2024 international conference on Communication, Computing and internet of Things, IC3IoT 2024

作者： Pavithra, M. Archana, D. Kanikha, M. Kavin, B. Kanishkar Rohith, G. Haritha, S. Kongu Engineering College Department Of Ece TamilNadu Erode638060 India Psg Institute Of Technology And Applied Research Department Of Ece Tamil Nadu Coimbatore India

ISBN: (纸本)9798350352689

Denoising X-ray images is essential for improving medical image quality, especially in diagnosing conditions like knee osteoarthritis in Computer-Aided Diagnosis (CAD) systems. The presence of statistical noise, such as Poisson noise, can degrade X-ray image quality. While increasing radiation intensity and detector exposure time can help reduce this corruption, certain scenarios require low-dose detection and rapid response, such as security checks and urgent medical imaging assessments. Balancing noise reduction with preserving essential edge details poses a complex challenge in the denoising process. This study introduces a hybrid approach that combines two distinct denoising techniques: Fused Wavelet Transform and Autoencoder networks. The fusion strategy aims to leverage the strengths of both methods to enhance denoising performance and accuracy. Standard image quality metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Peak signal-to-Noise Ratio (PSNR), Frequency based Error (FBE), Mean Squared Error (MSE), Structural Similarity Index Matrix (SSIM), and Contrast-to-Noise Ratio (CNR) are employed to evaluate performance. The proposed framework is rigorously evaluated, demonstrating significant improvements in denoising performance. Thus, the hybrid fusion method produces denoised images with higher detail and accuracy compared to conventional Convolutional Neural Network (CNN) based methods. © 2024 IEEE.

关键词： image quality

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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...

引用

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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