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

共 20446 条记录，以下是31-40 订阅

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AI-GENERATED image DETECTION WITH WASSERSTEIN DISTANCE COMPRESSION AND DYNAMIC AGGREGATION 31

AI-GENERATED IMAGE DETECTION WITH WASSERSTEIN DISTANCE COMPR...

引用

2024 international conference on image Processing

作者： Lyu, Zihang Xiao, Jun Zhang, Cong Lam, Kin-Man Hong Kong Polytech Univ Dept Elect & Elect Engn Kowloon Peoples R China

ISBN: (纸本)9798350349405;9798350349399

With the rapid advancement of generative models, image detectors for AI-generated content have become an increasingly necessary technology in computer vision, attracting significant attention from researchers. This technology aims to detect whether an image is naturally generated by imaging systems (e.g., digital cameras) or generated by advanced AI techniques. Despite the promising performance achieved by recent fake detection methods, they are typically trained on millions of redundant images with similar characteristics, leading to inefficient training. Furthermore, the performances of existing detectors often deteriorate when the training datasets are imbalanced. To address these challenges, we propose a novel AI-generated image detector based on dynamic aggregation and information compression with the Wasserstein distance. Experimental results show that our proposed method significantly outperforms state-of-the-art models that generalize across different generative models, with an increase of +1.86% average accuracy and +0.14% average precision, while substantially reducing the training time. On imbalanced datasets, our proposed method leads to a +14.46% accuracy improvement, clearly demonstrating its robustness on imbalanced datasets.

关键词： Fake image Detection Efficient Training

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Exploring Model Compression Techniques for Efficient 1D CNN-based Hand Gesture Recognition on Resource-Constrained Edge Devices 7

Exploring Model Compression Techniques for Efficient 1D CNN-...

引用

ieee 7th international conference on Advanced Technologies, signal and image Processing (ATSIP)

作者： Mnif, Mahdi Sahnoun, Salwa Djemaa, Mahdi Fakhfakh, Ahmed Kanoun, Olfa Univ Sfax Natl Sch Elect & Telecommun Sfax Sfax Tunisia Digital Res Ctr Sfax CRNS Lab Signals Syst Artificial Intelligence & Networ Sfax Tunisia Tech Univ Chemnitz Fac Elect Engn & Informat Technol Measurements & Sensor Technol Chemnitz Germany

ISBN: (纸本)9798350351491;9798350351484

Tiny Machine Learning is undergoing rapid evolution in the context of edge computing and intelligent internet of Things (IoT) devices. This paper investigates the potential of model compression techniques for enhancing energy efficiency within IoT edge devices, as an application, hand gesture recognition based on Electrical Impedance Tomography data was used. To achieve efficient and accurate machine learning on battery-powered devices, three model compression techniques: Global pruning, Knowledge Distillation (KD), and quantization were investigated and applied on a 1D convolutional neural network. The global pruning technique resulted in a slight improvement in test data accuracy while maintaining the model size at 284.45 kB, though it increased the model's sparsity. With KD, the model size significantly decreased to 75.042 kB, with negligible impact on accuracy. In contrast, 8-bit quantization reduced the model size to 78.434 kB, but this came at the cost of a substantial 13.75% decrease in accuracy. Each model compression technique contributes to reducing model size or increasing sparsity. This is particularly beneficial for deploying deep learning models on resource-constrained IoT devices.

关键词： Edge computing internet of Things Electrical Impedance Tomography Convolutional Neural Network Model Compression Pruning Knowledge Distillation Quantization

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Semantic Segmentation-based Label Position Defect Detection

Semantic Segmentation-based Label Position Defect Detection

引用

9th international conference on signal and image Processing (ICSIP)

作者： Yue, Hongwei Jin, Yingying Guangdong Univ Educ Sch Phys & Informat Engn Guangzhou Peoples R China Guangzhou Panyu Polytech Dept Gen Required Courses Guangzhou Peoples R China

ISBN: (纸本)9798350350920

In the label manufacturing industry, accurately identifying label positioning defects is of great significance for ensuring product quality. However, traditional defect detection techniques have faced significant challenges due to the diversity of label materials and the complexity of imaging conditions. In recent years, semantic segmentation technology has made breakthrough progress in the field of image processing, providing innovative solutions to the aforementioned issues. This study proposes a semantic segmentation framework that integrates ResNet50 and Global Context (GCNet) modules, aiming to accurately identify label positioning defects. Through the training of a deep learning model, this framework achieves precise delineation of the label area and effectively determines positioning defects. The experiment was conducted using a dataset composed of 1653 manually annotated images, and the results revealed that the proposed algorithm has significant advantages in terms of segmentation accuracy and algorithm robustness. The algorithm developed in this study demonstrates high efficiency in detecting label positioning defects, providing an innovative technical approach for automated quality control systems.

关键词： labels image segmentation attention mechanism GCNet

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CMA: A Cross-Modal Attack on Radar signal Recognition Model based on Time-Frequency Analysis 59

CMA: A Cross-Modal Attack on Radar Signal Recognition Model ...

引用

59th Annual ieee international conference on Communications (ieee ICC)

作者： Wang, Mengchao Zhang, Sicheng Xuan, Qi Lin, Yun Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Zhejiang Univ Technol Inst Cyberspace Secur Hangzhou 310023 Peoples R China

ISBN: (纸本)9781728190549

In recent years, with the rise of deep learning, it has become a hot research topic to combine time-frequency analysis technology with deep learning to recognize radar signals. For the application of deep learning in radar signal recognition, however, the discovery of adversarial examples poses a tremendous security risk. based on experiments, it appears that the radar signal recognition model based on the time-frequency image have been shown to be less vulnerable to adversarial attack methods based on time domain. Therefore, we propose a cross-modal attack (CMA). Firstly, we establish a surrogate model architecture locally, including three parts: time-frequency analysis, data quantization, and classifier. Secondly, we train this architecture as a whole and generate adversarial examples utilizing the trained surrogate model architecture parameters and adversarial attack methods. Finally, we carry out the CMA on the radar signal recognition model based on the time-frequency image by adding adversarial perturbations to the original signal. According to experimental results, the CMA can reduce the model recognition accuracy by more than 30%, demonstrating good attack performance, when the perturbation strength is 0.1 and the signal-to-noise ratio is 0 dB.

关键词： Radar signal recognition deep learning adversarial attack time-frequency analysis cross-modal attack

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(POSTER) Visual Transformer Models on UAV-Captured images for Person Detection 20

(POSTER) Visual Transformer Models on UAV-Captured Images fo...

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20th Annual international conference on Distributed Computing in Smart Sensor systems and the internet of Things (DCOSS-IoT)

作者： Zahia, Ionel-Gabriel Popescu, Dan Ichim, Loretta Natl Univ Sci & Technol POLITEHN Bucharest Fac Automat Control & Comp Sci Bucharest Romania

ISBN: (纸本)9798350369458;9798350369441

One of the most important applications of UAVs is person detection for security or rescue tasks. The goal of the proposed paper is to develop, experiment, and compare the performance of two new neural networks based on the transformer architecture, Detection Transformer and Vision Transformer. Two datasets were used, an own one for testing and COCO for learning. The results are promising to take into account the difficulties of person detection at a distance.

关键词： unmanned aerial vehicle person detection image processing neural networks performance metrics

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Deep Learning Enhanced Channel Estimation for the internet of Vehicles OFDM systems

Deep Learning Enhanced Channel Estimation for the Internet o...

引用

2024 ieee international conference on Computational Electromagnetics, ICCEM 2024

作者： Liu, Zheng Pan, Xiaomin School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350383317

The deep learning enhanced channel estimation is presented for the OFDM communication in the internet of Vehicles (10V). Using the image enhancement and denoising, the channel responses at unknown positions are predicted under a specified signal-to-Noise Ratio (SNR). Simulation experiments were conducted in predefined urban road *** results show that this method shows significant improvement compared to traditional methods in the minimum mean square error (MMSE) of channel estimation, with an average decrease of 0.03 in a series of experiments with different SNRs. © 2024 ieee.

关键词： Orthogonal frequency division multiplexing

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Research on the Application of image Preprocessing technology based on Computer Deep Learning in Wireless Communication systems 4

Research on the Application of Image Preprocessing Technolog...

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4th ieee international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Xu, Xiaoqing School of Information Engineering Eurasia University Shaanxi Xi'an China

ISBN: (纸本)9798350359589

This paper proposes a radio frequency signal identification method based on deep neural network. First, this article abstracts the radio frequency signal into a plane diagram and converts the radio frequency signal identification problem into object detection in image recognition. Then, it is combined with the latest international artificial intelligence technology to further enhance the intelligence of radio frequency signals. At the same time, the ability to identify complex electromagnetic interference is enhanced. Then this paper proposes a method to identify interference signals through radio spectrum images based on deep learning technology, and designs experiments to verify the identification accuracy of this method. The effectiveness of this method was verified through field sampling of a total of 5,432 samples of 15 categories. The proposed method can correctly identify the wave pattern and frequency characteristics of radio frequency signals, with an accuracy rate of 87.14%. The simulation results show that this method is universal and has good identification accuracy for interference at other frequency points. © 2024 ieee.

关键词： convolutional neural network deep network image object detection radio frequency learning Radio wave recognition

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Enhanced Spatial Domain image Steganography for Improved IoT Security and Privacy Applications 21

Enhanced Spatial Domain Image Steganography for Improved IoT...

引用

21st ieee international conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Septinaputri, Gracetriana Survinta D'Layla, Adifa Widyadhani Chanda De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Inst Teknol Sepuluh Nopember Dept Informat Surabaya Indonesia Univ Rwanda Kigali Rwanda

ISBN: (纸本)9798350363999;9798350364002

In today's interconnected world, ensuring robust security and privacy for IoT-enabled systems is essential due to the vulnerabilities introduced by the proliferation of IoT devices. Practical strategies, including data hiding for secure transmission, are crucial for protecting sensitive information and critical infrastructure. Addressing challenges such as device authentication and data access control is vital. As digital images become the most commonly used media in IoT applications, many practitioners prefer using them to secure confidential data transmission through steganography. To address data privacy issues in IoT applications, this paper introduces a novel steganography algorithm designed to minimize image distortion while optimizing the use of embeddable pixels in the cover image. The proposed method converts secret data into a simplified format and sorts the binary representations of secret bits in ascending order, reducing the size of the secret data and significantly decreasing the payload. Experimental results demonstrate that this method outperforms existing techniques, achieving a Peak signal-to-Noise Ratio (PSNR) of 67.42 dB and an average Structural Similarity Index Measure (SSIM) of 0.99, ensuring a high-quality stego image. This advancement is believed to significantly enhance the security and privacy of IoT data transmissions.

关键词： Data hiding data privacy Securing IoT data transmission spatial domain images steganography

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Restoration of Distorted Laser-Spot image based on Generative Adversarial Networks 13

Restoration of Distorted Laser-Spot Image based on Generativ...

引用

13th ieee international conference on Communications, Circuits and systems (ICCCAS)

作者： Diao, Shuiqiu Li, Jianjun Zhang, Rui Wen, Feng Wu, Baojian Qiu, Kun Univ Elect Sci & Technol China Sch Informat & Commun Engn Key Lab Opt Fiber Sensing & Commun Minist Educ Chengdu Peoples R China

ISBN: (纸本)9798350386288;9798350386271

We introduce the generative adversarial networks (GANs) into the application of the free-space laser communication, performing the laser-spot image restoration with the computer-vision method. After the training of our model is completed, the average Pearson correlation coefficient, average peak signal-to-noise ratio and mean-square-error of the generated restored laser spot image reach 0.964, 27.713dB and 0.003, respectively. According to the numerical results, the GAN method could effectively resist the interference of atmospheric turbulence, and accurately restore the amplitude distribution of the laser captured from the free-space channel.

关键词： laser spot image restoration deep learning

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MACHINE-ATTENTION-based VIDEO CODING FOR MACHINES 30

MACHINE-ATTENTION-BASED VIDEO CODING FOR MACHINES

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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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