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image-based Feature Representation for Enhanced Cyber-Attack Detection in IoHT Environments 1

Image-based Feature Representation for Enhanced Cyber-Attack...

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1st international conference on Emerging Technologies for Dependable internet of Things, ICETI 2024

作者： Al-Azzani, Abdoualwase M. Al-Shalabi, Ahmed Abdullah Ghurab, Mossa Alhrazi, Firdaus Alhomdy, Sharaf A. Algabri, Malek Department of Computer Science Sana’a University Sana’a Yemen Department of Information Technology Sana’a University Sana’a Yemen

ISBN: (纸本)9798331533557

The complexity of cybersecurity risks has expanded dramatically with the fast expansion of the internet of Health Things (IoHT), especially with regard to cyberattacks that target healthcare systems. In healthcare settings, data privacy and integrity are very important, hence developing improved detection techniques that can reliably identify fraudulent activity is urgently needed. In order to improve cyber-attack detection in IoHT, this research presents an image-based feature representation technique that makes use of convolutional neural networks (CNNs), notably the MobileNet architecture. With the use of the ECU-IoHT dataset’s network traffic statistics, the suggested model is able to catch intricate patterns that point to infiltration. Because MobileNet-CNN computation is efficient, it can be deployed in real-time in low-resource situations, which are typical of IoHT installations. Our method outperforms current state-of-the-art methods, with lower false positive and negative rates and better classification accuracy of 99.1%, precision of 99.00%, recall of 97.1%, and F1-score of 98.4%. The findings highlight how image-based deep learning models may improve cybersecurity safeguards in internet of Things frameworks by offering a powerful instrument for proactive threat identification and removal. This study lays the groundwork for future studies that will use image-based representations of features for real-time threat identification, furthering the area of IoHT cybersecurity. © 2024 ieee.

关键词： Cyber attacks

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Evaluation of Deep Learning Approaches for Air Quality Analysis using an image Dataset 2

Evaluation of Deep Learning Approaches for Air Quality Analy...

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2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Kaushal, Rajanish Kumar Pandey, Himani Kalaiarasi, G. Arya, Sujata Rawat, Devyani Natrayan, L. Chandigarh Univ Dept Elect Engn Mohali Punjab India ITM SLS Baroda Univ Dept Appl Sci & Humanities Vadodara 391510 Gujarat India Vignans Fdn Sci Technol & Res Deemed Be Univ Dept Adv Comp Sci & Engn Guntur 522213 Andhra Pradesh India Symbiosis Int Deemed Univ SIU Fac Law Symbiosis Law Sch SLS Pune 411014 Maharashtra India Graph Era Deemed Be Univ Dept Management Studies Dehra Dun Uttarakhand India SIMATS Dept Mech Engn Saveetha Sch Engn Chennai 602105 Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Air pollution has caused widespread concern, particularly in developing nations such as India and China. Despite the availability of expensive or inaccurate methods such as sensor-based or community network-based approaches, image-based air pollution evaluation still requires significant improvement. In response to this pressing issue, our research develops an accurate Deep Learning (DL) model for estimating air quality from environmental images. We employed Visual Geometry Group 19 (VGG-19) and Residual Network 50 (ResNet-50) as DL models. The air quality dataset was collected from the internet and grouped into three categories: Good, Moderate, and Severe, based on the Particulate Matter (PM) values. image pre-processing techniques such as resizing and rescaling were used. The DL models were evaluated using accuracy, loss, and time metrics. ResNet-50 was identified as the best model for identifying air quality based on the images. The achieved accuracy level by ResNet-50 is 97.87%, and it takes 45.02 seconds to analyze the test data count of 90 images. This shows the effectiveness of the ResNet-50 model in air quality prediction.

关键词： Air Quality Deep Learning Transfer Learning images Training Resize

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image2POINTS: A 3D POINT-based CONTEXT CLUSTERS GAN FOR HIGH-QUALITY PET image RECONSTRUCTION 49

IMAGE2POINTS: A 3D POINT-BASED CONTEXT CLUSTERS GAN FOR HIGH...

引用

49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Cui, Jiaqi Wang, Yan Wen, Lu Zeng, Pinxian Wu, Xi Zhou, Jiliu Shen, Dinggang Sichuan Univ Sch Comp Sci Chengdu Peoples R China Chengdu Univ Informat Technol Sch Comp Sci Chengdu Peoples R China ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Shanghai United Imaging Intelligence Co Ltd Dept Res & Dev Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

To obtain high-quality Positron emission tomography (PET) images while minimizing radiation exposure, numerous methods have been proposed to reconstruct standard-dose PET (SPET) images from the corresponding low-dose PET (LPET) images. However, these methods heavily rely on voxel-based representations, which fall short of adequately accounting for the precise structure and fine-grained context, leading to compromised reconstruction. In this paper, we propose a 3D point-based context clusters GAN, namely PCC-GAN, to reconstruct high-quality SPET images from LPET. Specifically, inspired by the geometric representation power of points, we resort to a point-based representation to enhance the explicit expression of the image structure, thus facilitating the reconstruction with finer details. Moreover, a context clustering strategy is applied to explore the contextual relationships among points, which mitigates the ambiguities of small structures in the reconstructed images. Experiments on both clinical and phantom datasets demonstrate that our PCC-GAN outperforms the state-of-the-art reconstruction methods qualitatively and quantitatively. Code is available at https://***/gluucose/PCCGAN.

关键词： PET reconstruction context clusters point-based representation GAN

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Explaining Representations in Correlation-based Deep Multiview Representation Learning

Explaining Representations in Correlation-based Deep Multivi...

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

作者： Kuschel, Maurice Alkhatib, Amr Hasija, Tanuj Boström, Henrik Signal and System Theory Group Paderborn University Paderborn Germany Division of Software and Computer Systems KTH Royal Institue of Technology Stockholm Sweden

ISBN: (纸本)9798350368741

Multiview representation learning techniques based on deep correlation maximization have become increasingly popular for learning meaningful and compact representations from multiview data. Even though their performance is state-of-the-art in many interpretability-critical fields, their black-box behavior poses a problem and restricts their usability. To overcome this restriction, we propose XDCCA (eXplanations for Deep Canonical Correlation Analysis), an explanation strategy using characteristic rules in combination with SHAP that exploits the inherent structure of latent spaces created by correlation maximization techniques. We demonstrate how XDCCA allows for interpreting learned representations and their correlation using real medical time series and synthetic image data. © 2025 ieee.

关键词： CEGA Deep Canonical Correlation Analysis Explainable AI Multiview Representation Learning SHAP

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Analog Features Extractor for Ultra-Low Power Embedded AI Listening and Keyword Spotting 6

Analog Features Extractor for Ultra-Low Power Embedded AI Li...

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6th international conference on AI Circuits and systems (AICAS)

作者： Marzetti, Sebastian Gies, Valentin Barchasz, Valentin Barthelemy, Herve Glotin, Herve Aix Marseille Univ Univ Toulon IM2NP CNRSUMR 7334 Marseille France Aix Marseille Univ Univ Toulon CNRS LISDYNI Marseille France Univ Toulon & Var Ctr Intelligence Artificielle Acoust Nat Toulon France

ISBN: (纸本)9798350383638;9798350383645

In this paper, a novel ultra-low power audio system is designed and tested on keyword spotting (KWS). It implements mixed (analog-digital) processing and allows always-on detection with an average power consumption of < 77 mu W, detecting one word every 30 seconds. Combining mixed signal processing, it achieves 88% classification accuracy between 8 classes. It is based on an always-on Analog Features Extractor (AFE), which provides spectral information, and main processor wakes-up only when necessary for minimal power consumption. Then, this information is sampled by a low frequency ADC to compose a spectrogram processed with a Convolutional Neural Network (CNN) for classification. It can be used for long term environmental monitoring or intelligent internet of Things (IoT) using small batteries such as a single CR2032 coin cell to reach up to 1 year of autonomy.

关键词： Ultra Low-Power Keyword Spotting signal Processing Embedded System Mixed signal processing Analog Digital Embedded Artificial Intelligence Machine Learning Voice Detection Long Term Monitoring Soundscape Monitoring

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LEARNING DISCRIMINATIVE STYLE REPRESENTATIONS FOR UNSUPERVISED AND FEW-SHOT ARTISTIC PORTRAIT DRAWING GENERATION 49

LEARNING DISCRIMINATIVE STYLE REPRESENTATIONS FOR UNSUPERVIS...

引用

49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Fang, Junkai Fang, Nan Huang, Fei Zhou, Jinglin Qiao, Maoying Gao, Fei Alibaba Grp Hangzhou Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou Peoples R China Univ Technol Sydney Sydney NSW Australia Xidian Univ Hangzhou Inst Technol Xian Peoples R China

ISBN: (纸本)9798350344868;9798350344851

In this paper, we propose an unsupervised artistic portrait drawing generation method for few-shot datasets based on contrastive learning of style features. Firstly, we construct a discriminative style encoder with contrastive learning, improving the ability of the encoder to separate style features. Secondly, based on the dynamic codebook and momentum network, we used historical average features instead of batch instance features to prevent the problem of style bias in few-shot datasets. Finally, a conditional projection discriminator with filter response normalization is utilized to improve the discriminative ability of the discriminator and the stability of the generative adversarial network, which motivates the generator to synthesize more realistic image details. Quantitative and qualitative analysis show that the method proposed in this paper significantly improves the quality of artistic portrait drawing generation, and outperforms existing benchmarks in terms of visual effect and metrics evaluation. Our code and results are avilable at https://***/AiArt-HDU/Co-GAN.

关键词： artistic portrait drawing generation face sketch synthesis few-shot learning image-to-image translation contrastive learning

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HYPERSPECTRAL image RECONSTRUCTION USING HIERARCHICAL NEURAL ARCHITECTURE SEARCH FROM A SNAPSHOT image 49

HYPERSPECTRAL IMAGE RECONSTRUCTION USING HIERARCHICAL NEURAL...

引用

49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Han, Xian-Hua Jiang, Huiyan Chen, Yen-Wei Rikkyo Univ Grad Sch Aritificial Intelligence & Sci Tokyo 1718501 Japan Northeastern Univ Software Coll Shenyang Peoples R China Ritsumeikan Univ Kyoto Japan

ISBN: (纸本)9798350344868;9798350344851

Hyperspectral imaging is a promising imaging modality, and has attracted increasing research attention by compressive sensing such as coded aperture snapshot spectral imaging (CASSI), for simultaneously capturing abundant information in spatial, spectral and temporal domains. Hyperspectral image (HSI) reconstruction in the CASSI aims to retrieve the original 3D signal upon the 2D compressed snapshot. Recently, deep learning has extensively been employed for HSI reconstruction via manually designing network architectures, and usually causes complicated and massive-computational models, which are difficult for being embedding in the real imaging systems. This study aims to leverage network architecture search to automatically design effective and efficient network architectures for HSI reconstruction. Specifically, we exploit gradient-based search strategies and prepare optional operations (cells) with adaptive receptive field such as dilate and deformable convolutional layers to construct a flexible hierarchical search space. Through sharing cells within different levels of features and utilizing an early stopping technique, we achieve a computational and memory efficient NAS strategy to automatically design an effective lightweight model for HSI reconstruction. Extensive experimental results have demonstrated that the network architecture achieved by our proposed NAS has much smaller model size and a lower computational cost while produce better or comparable HSI reconstruction performance compared with the state-of-the-art methods.

关键词： Hyperspectral image reconstruction Network architecture search Gradient-based search Hierarchical search space Lightweight model

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MULTI-EXIT VISION TRANSFORMER WITH CUSTOM FINE-TUNING FOR FINE-GRAINED image RECOGNITION 30

MULTI-EXIT VISION TRANSFORMER WITH CUSTOM FINE-TUNING FOR FI...

引用

30th ieee international conference on image Processing (ICIP)

作者： Shen, Tianyi Lee, Chonghan Narayanan, Vijaykrishnan Penn State Univ Dept Elect Engn & Comp Sci State Coll PA 16801 USA

ISBN: (纸本)9781728198354

Capturing subtle visual differences between subordinate categories is crucial for improving the performance of Fine-grained Visual Classification (FGVC). Recent works proposed deep learning models based on Vision Transformer (ViT) to take advantage of its self-attention mechanism to locate important regions of the objects and extract global information. However, their large number of layers with self-attention mechanism requires intensive computational cost and makes them impractical to be deployed on resource-restricted hardware including internet of things (IoT) devices. In this work, we propose a novel Multi-exit Vision Transformer architecture (MEViT) for early exiting based on ViT, as well as a fine-tuning strategy that involves self-distillation to improve the accuracy of early exit branches on FGVC task compared to the baseline ViT model. The experiments on two standard FGVC benchmarks show our proposed model provides superior accuracy-efficiency trade-offs compared to the state-of-the-art (SOTA) ViT-based model and demonstrate that it is possible to accurately classify many subcategories with significantly less effort.

关键词： Distillation

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A wandb based corrosion detection of metals through live video analytics 2

A wandb based corrosion detection of metals through live vid...

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2nd ieee international conference on IoT, Communication and Automation technology, ICICAT 2024

作者： John Deva Prasanna, D.S. Sai Neeraj, Kuruvella Jaya Krishna, G. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of DSBS India SRM Institute of Science and Technology Department of DSBS India

ISBN: (纸本)9798350368109

Detection of corrosion in moving objects like ships is challenging due to the dynamic nature of the input image. Existing machine learning techniques are suitable for static images and the algorithms suffer in performance when is a live video. In this paper, image processing for detecting corrosion using YOLOv8 which more suitable for processing live videos as speed and accuracy is better. This makes YOLO v8 for corrosion detection in live videos. In addition, Weights and Biases (W&B). is used in the algorithm as it is pivotal in establishing the connections between neurons and biases helps in circumventing flexible inputs. By combining YOLOv8's and W&B approach the accuracy and efficiency of corrosion detection systems is improved. This can ultimately assist in better maintenance and preservation of essential infrastructure resources. © 2024 ieee.

关键词： Video signal processing

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LIDAR DEPTH MAP GUIDED image COMPRESSION MODEL 31

LIDAR DEPTH MAP GUIDED IMAGE COMPRESSION MODEL

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2024 international conference on image Processing

作者： Gnutti, Alessandro Della Fiore, Stefano Savardi, Mattia Chen, Yi-Hsin Leonardi, Riccardo Peng, Wen-Hsiao Univ Brescia CNIT Dept Informat Engn Brescia Italy Univ Salerno Dept Informat Salerno Italy Univ Brescia DSMC Brescia Italy Natl Yang Ming Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9798350349405;9798350349399

The incorporation of LiDAR technology into some high-end smartphones has unlocked numerous possibilities across various applications, including photography, image restoration, augmented reality, and more. In this paper, we introduce a novel direction that harnesses LiDAR depth maps to enhance the compression of the corresponding RGB camera images. To the best of our knowledge, this represents the initial exploration in this particular research direction. Specifically, we propose a Transformer-based learned image compression system capable of achieving variable-rate compression using a single model while utilizing the LiDAR depth map as supplementary information for both the encoding and decoding processes. Experimental results demonstrate that integrating LiDAR yields an average PSNR gain of 0.83 dB and an average bitrate reduction of 16% as compared to its absence.

关键词： Learned image compression LiDAR depth map prompts transformer

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