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

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Robustness of Deep Learning enabled IoT Applications Utilizing Higher Order QAM in OFDM image Communication System 5

Robustness of Deep Learning enabled IoT Applications Utilizi...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Islam, Nazmul Ahmad, Ijaz Shin, Seokjoo Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

ISBN: (纸本)9781665456456

In a standard Orthogonal Frequency Division Multiplex (OFDM)-based communication system, higher order M-ary Quadrature Amplitude Modulation (M-QAM) increases throughput and link capacity significantly due to the larger number of bits per symbol. Such a system is suitable to transmit large volumes of image data for real-time internet of Things (IoT) applications. However, higher order M-QAM increases the noise margin and Bit Error Rate (BER) in the received signal. Although the error bits corrupt the individual pixels values, the global contents of the image may remain intact and can be interpreted by Deep Learning (DL) models. Therefore, the current study analyzes the robustness of DL models for image communication systems utilizing higher order M-QAM. The performance analysis of the system is presented in terms of the BER and image quality measures. In the analysis, distortions in the reconstructed images decrease significantly when using higher E-b/N-0 across all M-QAM. For higher E-b/N-0 in the 1024-QAM system, the performance of the model improved by 11% and obtained an accuracy of 86%, lagging by 6% compared to the accuracy obtained on the original images. Overall, the model maintained a high accuracy range between 83% and 90% at E-b/N-0 10 dB across all M-QAM.

关键词： OFDM System QAM Modulation Deep learning

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VulSAT: Source Code Vulnerability Detection Scheme based on SAT Structure 8

VulSAT: Source Code Vulnerability Detection Scheme Based on ...

引用

8th international conference on signal and image Processing, ICSIP 2023

作者： Xue, Jintao Yu, Zihan Song, Yubo Qin, Zhongyuan Sun, Xin Wang, Wen Southeast University School of Cyber Science and Engineering Nanjing211189 China State Grid Zhejiang Electric Power Co. Ltd. Research Institute Internet Technology Center Hangzhou310063 China State Grid Zhejiang Electric Power Co. Ltd Internet Department Hangzhou310063 China

ISBN: (纸本)9798350397932

The number of software vulnerabilities have increased rapidly, and their forms have shown the characteristics of complexity and diversity, which has brought severe challenges to software systems. Deep learning can automatically learn the features of code and is widely used in source code vulnerability detection. Some studies treat codes as text sequences, ignoring the structural relationship between lines of code. Other studies extract the code as a graph structure, but ignore the order relationship of code lines and do not make full use of the dependencies between code lines. This paper proposes a C/C++ source code vulnerability detection scheme based on SAT (Structure-Aware Transformer) structure. It first standardizes the source code, then extracts the PDG (program dependency graph) representation of source code, next convert each node into a vector representation using sentence embedding, which retains the dependencies between nodes. Finally input it into the SAT architecture model for training, and judge whether the input program contains vulnerabilities according to the output results. We compared VulSAT with a variety of vulnerability detection methods (i.e., Devign, SySeVR, VulCNN, Checkmarx, VulDeePecker), and selected C/C++ functions in the SARD vulnerability dataset as experimental objects. The performance of accuracy rate and false positive rate has been improved to a certain extent. © 2023 ieee.

关键词： Deep learning

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Design and Performance Evaluation of an Intelligent Clothing Quality Inspection System Combining Artificial Intelligence and the internet of Things 4

Design and Performance Evaluation of an Intelligent Clothing...

引用

4th ieee international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Liu, Yushan Li, Xin Changchun Humanities and Sciences College Jilin Changchun China

ISBN: (纸本)9798350352931

In the process of clothing production, changes in environmental factors such as lighting, temperature, and humidity can affect image quality and detection accuracy, but existing research often does not fully consider these factors. Therefore, this article combines the image recognition technology of artificial intelligence algorithms and the internet of Things to design a clothing quality inspection system. Firstly, a clothing image preprocessing method based on smooth filtering is studied, and an improved CBAM (Convolutional Block Attention Module) mechanism is introduced into the Convolutional Neural Network (CNN) algorithm to adjust the attention of the algorithm's information channels and regions. Secondly, the Laplacian variance method is used to measure the clarity of image samples, and multiple clothing samples from the DeepFashion database are selected for algorithm training and quality inspection experiments. The experimental results show that the introduction of channel attention mechanism (CA) significantly improves the accuracy of the model, reaching between 93.8% and 95.2%. In addition, the test results showed that wrinkles accounted for 37% of clothing defects and thread ends accounted for 30%, indicating that wrinkles and thread ends are the most common types of defects. Taking T-shirts as an example, the accuracy of artificial intelligence detection is 95%, which is higher than the 93% of manual detection, an increase of 2 percentage points. © 2024 ieee.

关键词： Convolutional neural networks

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Cybernated Watermarking Approach on image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

引用

2nd international conference on Computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 ieee.

关键词： Cryptography

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Dependable Pipeline Detection algorithms for GPRs in Obscuration Environments 7

Dependable Pipeline Detection algorithms for GPRs in Obscura...

引用

7th ieee international conference on Electronic Information and Communication technology, ICEICT 2024

作者： Zhou, Hongyu Xu, Guanghui Li, Yingsong Huang, ZhiXiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350384437

We propose a robust pipeline detection algorithm for obscuration environments, which includes two improvements: a pre-processing method called the Regional Adaptive Thresh-olding Algorithm (RATA) and a novel clustering method based on trend analysis and symmetric matching called as Symmetry Matching Clustering Algorithm (SMCA). This approach is highly effective in extracting targets from low-intensity regions and has been validated through simulation experiments. The gotten results demonstrate significant enhancements in the accuracy and reliability of underground pipeline detection in obscuration environments. Comparative analysis shows that, in some aspects, the proposed method outperforms existing algorithms. © 2024 ieee.

关键词： image enhancement

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Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations 48

Interpretation of Neural Networks is Susceptible to Universa...

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48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Oskouie, Haniyeh Ehsani Farnia, Farzan Sharif University of Technology Department of Computer Engineering Iran The Chinese University of Hong Kong Department of Computer Science & Engineering Hong Kong

ISBN: (纸本)9781728163277

Interpreting neural network classifiers using gradient-based saliency maps has been extensively studied in the deep learning literature. While the existing algorithms manage to achieve satisfactory performance in application to standard image recognition datasets, recent works demonstrate the vulnerability of widely-used gradient-based interpretation schemes to norm-bounded perturbations adversarially designed for every individual input sample. However, such adversarial perturbations are commonly designed using the knowledge of an input sample, and hence perform sub-optimally in application to an unknown or constantly changing data point. In this paper, we show the existence of a Universal Perturbation for Interpretation (UPI) for standard image datasets, which can alter a gradient-based feature map of neural networks over a significant fraction of test samples. To design such a UPI, we propose a gradient-based optimization method as well as a principal component analysis (PCA)-based approach to compute a UPI which can effectively alter a neural network's gradient-based interpretation on different samples. We support the proposed UPI approaches by presenting several numerical results of their successful applications to standard image datasets. © 2023 ieee.

关键词： image recognition

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A High Quality Secure Medical image Steganography Method 3

A High Quality Secure Medical Image Steganography Method

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3rd international conference on Computing and Information technology, ICCIT 2023

作者： Hameed, Mohamed Abdel Hassaballah, M. Bekhet, Saddam Narmatha, C. Kenk, Mourad A. Luxor University Faculty of Computers and Information Department of Computer Science Luxor Egypt Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Department of Computer Science AlKharj Saudi Arabia South Valley University Faculty of Commerce Qena Egypt University of Tabuk Faculty of Computers and Information Technology Tabuk City Saudi Arabia South Valley University Faculty of Computers and Artificial Intelligence Department of Computer Science Hurghada Egypt

ISBN: (纸本)9798350321487

The exchange of medical images over the internet has prompted substantial claims on the last years due to the presentation of cloud-based medical data systems according to various web services in the area of telemedicine applications. The preservation of confidential information has ever been a key indicator of the effectiveness of these systems. In this paper, an efficient data hiding technique is proposed for Digital Imaging and Communications in Medicine (DICOM) images hence security can be specified for the highly invaluable and private sensitive data of the patient. The proposed method is developed for DICOM images to embed Patient Record Information (PRI) for maintaining the fidelity of both the critical components of the image and private patient data. The proposed method is very effective in telemedicine applications as it can increase the security of the PRI during its transmission over the internet from different remote places. © 2023 ieee.

关键词： Steganography

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Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for image Restoration Models Compression

Soft Knowledge Distillation with Multi-Dimensional Cross-Net...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Zhang, Yongheng Yan, Danfeng State Key Laboratory of Networking and Switching Technology BUPT Beijing China

ISBN: (纸本)9798350368741

Transformer-based encoder-decoder models have achieved remarkable success in image-to-image transfer tasks, particularly in image restoration. However, their high computational complexity-manifested in elevated FLOPs and parameter counts-limits their application in real-world scenarios. Existing knowledge distillation methods in image restoration typically employ lightweight student models that directly mimic the intermediate features and reconstruction results of the teacher, overlooking the implicit attention relationships between them. To address this, we propose a Soft Knowledge Distillation (SKD) strategy that incorporates a Multi-dimensional Cross-net Attention (MCA) mechanism for compressing image restoration models. This mechanism facilitates interaction between the student and teacher across both channel and spatial dimensions, enabling the student to implicitly learn the attention matrices. Additionally, we employ a Gaussian kernel function to measure the distance between student and teacher features in kernel space, ensuring stable and efficient feature learning. To further enhance the quality of reconstructed images, we replace the commonly used L1 or KL divergence loss with a contrastive learning loss at the image level. Experiments on three tasks-image deraining, deblurring, and denoising-demonstrate that our SKD strategy significantly reduces computational complexity while maintaining strong image restoration capabilities. © 2025 ieee.

关键词： contrastive learning image restoration Knowledge distillation multi-dimensional cross-net attention

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Multimodal medical image fusion based on edge enhancement and detail preservation

Multimodal medical image fusion based on edge enhancement an...

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

作者： Li, Mengya Zhao, Zhe Lin, Yusong School of Cyber Science and Engineering Zhengzhou University Zhengzhou China Collaborative Innovation Center for Internet Healthcare Zhengzhou University Zhengzhou China Hanwei IoT Institute Zhengzhou University Zhengzhou China

ISBN: (纸本)9781510680425

We propose a multimodal medical image fusion method based on edge enhancement and detail preservation to address the issue of lost image details in current medical image fusion approaches. Firstly, we decompose the images from different modalities using the non-subsampled shearlet transform (NSST) to obtain their respective high-frequency and low-frequency subbands. Next, we employ a novel detail-preserving parameter adaptive pulse coupled neural network to fuse the high-frequency subbands, and an edge-enhanced weighted local energy method to fuse the low-frequency subbands. Finally, the fused image is reconstructed by performing inverse NSST on the fused high-frequency and low-frequency subbands. Experimental results demonstrate that our proposed method effectively preserves more detailed information from the source images and exhibits excellent performance in both subjective and objective evaluations when compared to seven commonly used multimodal medical image fusion methods. © 2024 SPIE.

关键词： image fusion

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Research on abnormal behavior detection algorithm of internet of Things devices with computer vision assistance 7

Research on abnormal behavior detection algorithm of Interne...

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7th ieee international conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Yuan, Limin Zhang, Wancai Zhang, Nan Yu, Jun Yang, Wenqing Zhang, Wei Sun, Zhen Wang, Tao China of Limited Company of Jilin Province Power Communication Company Changchun China Nari Technology Co. Ltd. Nanjing China

ISBN: (纸本)9798350350760

Convolutional Random Forest Vision (CRFV) is an innovative technology that cleverly incorporates Convolutional Neural networks from the field of Computer Vision (CV). The Random Forest (RF) algorithm in machine learning aims to provide a powerful and efficient solution for abnormal behavior detection of internet of Things (IoT) devices. The CNN layer is responsible for front-end feature extraction and uses its depth structure to accurately capture complex patterns in image and video data, while RF integrates multiple decision trees at the back-end and uses its powerful classification and regression capabilities to classify and identify anomalies in high-dimensional features extracted by CNN. This not only makes full use of the advantages of CNN in processing visual data, but also plays into the advantages of RF in processing high-dimensional feature Spaces and reducing overfitting. It is found that CRFV not only enhances the recognition accuracy of complex and hidden abnormal behavior, but also improves the generalization ability of the model and reduces the false positive rate. It can not only effectively identify and warn the abnormal operation of IoT devices, but also provide advanced intelligent tools for maintaining network security, preventing failures and optimizing equipment management, and is an important step towards autonomous and intelligent IoT monitoring. ©2024 ieee.

关键词： Convolutional neural networks

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