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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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Machine Learning based Detection System for Identifying Deepfake images and Social Bots 6

Machine Learning Based Detection System for Identifying Deep...

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6th international conference on Advancements in Computing, ICAC 2024

作者： Liyanage, Chamath Dematgoda, Hirushini Harindi, Dewmini Perera, Nirmal Fernando, Harinda Rathnayake, Samadhi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This paper presents a machine learning-based approach to detect two major forms of misinformation on social media platforms: deepfake images and social bots. For deepfake image detection, we propose a novel hybrid model combining DenseNet121 with custom Convolutional Neural Networks, achieving 97% accuracy on a dataset of 140,000 images. The social bot detection component utilizes a dual-branch architecture, processing tweet content through LSTM networks and user metadata through dense layers, attaining 91 % accuracy on the CIC truth seeker dataset. Both models demonstrate balanced performance across classes, addressing the challenges of evolving misinformation tactics. By integrating these detection systems, we aim to enhance the integrity and reliability of information ecosystems in digital spaces. Our approach contributes to the ongoing efforts to combat the spread of misinformation on social media platforms. © 2024 ieee.

关键词： Bot (internet)

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Enhanced Deep Dehazing for Haze Removal in License Plates 3

Enhanced Deep Dehazing for Haze Removal in License Plates

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3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Suresh, Abishek Bharathi, R. Vijayakumar, Vaidehi SCOPE Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350372120

This paper introduces Enhanced Deep Dehazing (EDD), a novel deep learning model that overcomes the limitations of traditional image dehazing methods. Unlike approaches that rely on complex transmission map estimation, EDD utilizes an Encoder-Decoder architecture for direct data-driven learning. EDD effectively handles diverse hazy conditions that degrade image quality across various applications. It utilizes convolutional layers for feature extraction and deconvolutional layers for image reconstruction, employing ReLU activation and batch normalization to improve training stability and convergence. A case study on the application of EDD to car license plate dehazing, a critical task for autonomous vehicles and surveillance systems, is presented. EDD was trained and tested on the Foggy-Hazy Licence Plates dataset, with experimental results demonstrating superior efficiency compared to previous works, thus underscoring its effectiveness and potential in dehazing foggy/hazed license plates and highlighting its applicability in realworld scenarios. © 2024 ieee.

关键词： image reconstruction

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Distributed Pooled Data Intrusion Detection: Lessons Learned from Quantitative Group Testing 44

Distributed Pooled Data Intrusion Detection: Lessons Learned...

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44th ieee international conference on Distributed Computing systems (ICDCS)

作者： Hahn-Klimroth, Max Kaaser, Dominik Rau, Malin Goethe Univ Frankfurt Frankfurt Germany TU Hamburg Christian Doppler Lab Blockchain Technol Internet Hamburg Germany Univ Hamburg Hamburg Germany

ISBN: (纸本)9798350386066;9798350386059

The goal of (network) intrusion detection systems is to identify unauthorized or malicious activities within a computer network. In this work we consider the following theoretical model for intrusion detection systems in large data center networks. We assume that the network is modeled as a leaf-spine-architecture with m spine nodes and n leaves. In a sequence of observation periods, each spine node stores a snapshot of the communication graph and accumulates (an approximation of) the number of alerts caused by suspicious behavior. To identify the responsible malicious nodes, we apply a distributed reconstruction algorithm based on quantitative group testing: In quantitative group testing we are given a binary signal sigma of Hamming weight k along with a querying method. Each query pools multiple entries of s together and returns the sum of the entries in the pool. The goal is to reconstruct s using as few queries as possible. Our contributions in this paper are three-fold. First we mathematically analyze a distributed reconstruction algorithm for the quantitative group testing instance induced by our intrusion detection model. In particular, we analyze the performance assuming a communication graph where each leaf sends Geom(p) many packets to the spine nodes in each time interval, where p is a parameter of the model. Second, we prove that our algorithm achieves a performance that is optimal up to logarithmic factors. Finally, we simulate our approach and provide empirical data that show that our approach works well in practice. The main novelty of our analysis is that the test-design is given by the communication graphs that are accumulated in multiple observation periods. This is in contrast to classical group testing where the algorithm is allowed to decide on the test design, and we believe that our analysis of non-standard test designs is of independent interest to the distributed group testing community.

关键词： Reconstruction Algorithm Group Testing Pooled Data Intrusion Detection Leaf-Spine Architecture

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MULTI TASK-based FACIAL EXPRESSION SYNTHESIS WITH SUPERVISION LEARNING AND FEATURE DISENTANGLEMENT OF image STYLE 30

MULTI TASK-BASED FACIAL EXPRESSION SYNTHESIS WITH SUPERVISIO...

引用

30th ieee international conference on image Processing (ICIP)

作者： Lu, Wenya Peng, Zhibin Luo, Cheng Xie, Weicheng Wen, Jiajun Lai, Zhihui Shen, Linlin Shenzhen Univ Comp Vis Inst Sch Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781728198354

image-to-image synthesis paradigms have been widely used for facial expression synthesis. However, current generators are apt to either produce artifacts for largely posed and non-aligned faces or unduly change the identity information like AdaIN-based generator. In this work, we suggest to use image style feature to surrogate the expression cues in the generator, and propose a multi-task learning paradigm to explore this style information via the supervision learning and feature disentanglement. While the supervision learning can make the encoded style specifically represent the expression cues and enable the generator to produce correct expression, the feature disentanglement of content and style cues enables the generator to better preserve the identity information in expression synthesis. Experimental results show that the proposed algorithm can well reduce the artifacts for the synthesis of posed and non-aligned expressions, and achieves competitive performances in terms of FID, PNSR and classification accuracy, compared with four publicly available GANs. The code and pre-trained models are available at https://***/lumanxi236/MTSS.

关键词： Facial expression synthesis multi-task learning expression style learning style and content disentanglement

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Machine Learning Computational Design on the Edge Device in IoT Network to Classify Audience Face image Attention 8

Machine Learning Computational Design on the Edge Device in ...

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8th international conference on Information technology, Information systems and Electrical Engineering, ICITISEE 2024

作者： Budiana, Fitri Puspitasari Bandung, Yoanes School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350368970

The limitations of edge devices in processing capacity, storage, and memory can constrain the computational complexity of edge computing in extracting information directly from data sources. This information is then forwarded by the edge gateway to servers or the cloud, or to other edge devices, via the internet of Things (IoT) network for further data processing. A model in machine learning has a mathematical structure algorithmically, which represents complex computations in the implementation of artificial intelligence to automate problem-solving. In this research, with the Cross-Industry Standard Process for Data Mining (CRISP-DM) methods, the proposed design is a machine learning computing system on edge devices, using a Convolutional Neural Network (CNN) based model, specifically the MobileNetV2 architecture. The edge computing process with machine learning occurs on the ESP32-CAM, which is connected to a cloud storage system via the internet of Things (IoT) network. Machine learning computation on the ESP32-CAM is particularly advantageous for the classification of audience facial images during teaching and learning activities, that Captured by the ESP32-CAM, the images are categorized with labels of focused and unfocused. The image data, including its labels, is then sent to cloud storage. The dataset used in building the model consists of 94 training data and 35 testing data. Model testing was conducted 4 times, with a batch size of 256 and a total of 80 epochs. The total number of test data is 35, with 21 out of the 35 test image data correctly classified. Thus, the results obtained are 60% for accuracy, 66% for AUC, 72% for precision, 60% for recall, and 61 % for the F1 score. © 2024 ieee.

关键词： Convolutional neural networks

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Evaluating Deep Learning Approaches for Enhanced Underwater image Quality 4

Evaluating Deep Learning Approaches for Enhanced Underwater ...

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4th international conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Vairagar, Shubhangi Gupta, Praveen Sharma, R.P. Csmu Department of Computer Engineering Navi Mumbai India Dr. D. Y. Patil Institute of Technology Pimpri Pune India

ISBN: (纸本)9798331529635

This research investigates the application of deep learning techniques, including Deep Convolutional Neural Networks (DCNNs) and Conditional Generative Adversarial Networks (CGANs), for enhancing the quality of underwater images (UWIE). Underwater imaging presents significant challenges due to factors such as low light conditions, color distortion, and limited visibility. This study aims to improve the quality of underwater images by addressing these challenges through advanced image processing techniques. The performance of different UWIE methods is evaluated using metrics such as Mean Squared Error (MSE), Peak signal-to-Noise Ratio (PSNR), and Underwater image Quality Metrics (UIQM). The findings of this research contribute to the advancement of underwater imaging technology, enabling improved image quality for various applications, including marine biology, underwater exploration, and underwater archaeology. © 2024 ieee.

关键词： Convolutional neural networks

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Adaptive Compressive Sensing for Natural images Using Schur Decomposition and Optimized Reconstruction 4

Adaptive Compressive Sensing for Natural Images Using Schur ...

引用

4th international conference on Sustainable Expert systems, ICSES 2024

作者： Elaveini, M. Aarthi Deepa, T. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Ramapuram Chennai India

ISBN: (纸本)9798331540364

Compressive sensing (CS) is growing as an effective method for efficient imagine capture and recovery by harnessing the fundamental sparseness of natural images. The paper presents a unique framework for CS of live images employing Schur decomposition, a matrix decomposition approach that improves the detection and reconstruction processes. The suggested technique utilizes Schur decomposition to enhance the sensing matrix, facilitating more effective sampling by collecting essential imagine information in both spatial and frequency domains. The Schur-based sensing matrix, in contrast to conventional random sensing matrices, is designed to use the structural characteristics of real pictures, hence enhancing the precision of image reconstruction from a reduced quantity of observations. The integrated Schur decomposition with an iterative image reconstruction algorithm, incorporating Orthogonal Matching Pursuit (OMP) promotes sparsity. The combination improves the robustness of the recovery process while reducing reconstruction artifacts. Extensive simulations demonstrate that proposed Schur-based framework achieves superior performance compared to conventional CS techniques, particularly in terms of Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The proposed method improves the quality of reconstructed images and expedites the convergence of the reconstructing technique, hence reducing computational complexity. The framework is assessed over several natural imagine datasets, demonstrating substantial improvements in image quality and computing efficiency resulting in a PSNR of 37.263 dB and SSIM 0.813 for CAT image. The Schur decomposition method for CS is an effective solution for applications necessitating quick picture capture and high-quality reconstruction, including medical imaging, remote sensing, and real-time image processing systems. © 2024 ieee.

关键词： image reconstruction

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Concurrent Transmission for Low-Power Symbiotic Backscatter Communications: Spatial Modulation and Detector Design 24

Concurrent Transmission for Low-Power Symbiotic Backscatter ...

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24th ieee international conference on Communication technology, ICCT 2024

作者： Zhang, Jing-Kai Yang, Gang Zou, Xianbing Wu, Qingqing Zhong, Cheng Shenzhen China Sias National Key Laboratory of Wireless Communications Uestc Chengdu China National Key Laboratory on Communications Uestc Chendu China State Grid Smart Grid Research Institute Co. Ltd Beijing China State Grid Xiongan New Area Electric Power Supply Company Hebei China

ISBN: (纸本)9798350363760

Backscatter communication, which enables a battery-free backscatter tag to transmit information by using incident radio frequency (RF) signal as the carrier, is a promising solution for future green internet of Things. In this paper, we consider a symbiotic backscatter communication system with multiple tags, in which the multi-antenna tags employ spatial modulation for concurrent transmission to improve spectral efficiency. We propose a joint message-passing detector to recover both the RF source signal and the multiple tag signals simultaneously. Simulation results verify that the proposed algorithm achieves better bit error rate performance, compared with the existing detection algorithm based on successive interference cancellation. © 2024 ieee.

关键词： signal modulation

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Exploring Advanced image Refinement with Blind Denoising Autoencoder Architecture 2

Exploring Advanced Image Refinement with Blind Denoising Aut...

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

作者： Reddy, V Sagar Reddy, G Venkata Viswas Mohan, K Pradhyuth Raj, A Chetan Sai VNRVJIET Dept of ECE Hyderabad India

ISBN: (纸本)9798350366570

This project introduces an innovative method for enhancing license plate images by employing a blind autoencoder-based denoising and deblurring technique. Unlike conventional approaches that heavily rely on labeled training data, which can be both scarce and costly to obtain, our method circumvents this requirement. Instead, it leverages a blind autoencoder trained on a vast collection of unlabeled license plate images to effectively denoise and deblur input images. Through evaluation on a standard dataset, our approach demonstrates significant enhancements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to conventional methods. © 2024 ieee.

关键词： Optical character recognition

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Hybrid Approach to Enhance Security and Friendliness of Visual Secret Sharing Scheme 10

Hybrid Approach to Enhance Security and Friendliness of Visu...

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10th international conference on Emerging Trends in Engineering and technology - signal and Information Processing, ICETET-SIP-22 2022

作者： Panchbhai, Vishal V. Varade, Suchita W. Priyadarshini College of Engineering Department of Electronics & Telecommunication Engineering Nagpur India

ISBN: (纸本)9781665467414

In Block based Progressive Visual Secret Sharing (BPVSS) scheme, secret image will be recovered block by block from noise-like and meaningful shares. It is found that only meaningful shares are user-friendly. But the meaningful shares generated through BPVSS techniques show poor visual quality so it is suspicious to intruder while transmission over internet. Thus, this paper proposes a meaningful share generating strategy based on BPVSS and LSB-based steganography to solve the above-mentioned problem with less processing power and time. The proposed system delivers superior standard performance parameter readings and has various advantages, including (1) it is suited for grayscale as well as color secret images, (2) the contrast of the recovered image is the same as the original secret image, and (3) computational power and time required to recover secret image is very less. © 2022 ieee.

关键词： Visualization Steganography image color analysis Information processing Gray-scale Market research Hybrid power systems

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