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Retinex by Autoencoders 16

Retinex by Autoencoders

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16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Pezzoni, Claudio Mio, Corrado Barsotti, Annalisa Univ Milan Dipartimento Informat Giovanni degli Antoni Via Celoria 18 I-20133 Milan Italy Khalifa Univ Sci & Technol EBTIC Abu Dhabi City U Arab Emirates Univ Milan Dipartimento Informat I-20133 Milan Italy

ISBN: (纸本)9781665464956

The Retinex algorithms find wide applications as image enhancers, for their capability of preserving edges, while at the same time attenuating smooth gradients and chromatic dominants. They are characterized by the fact that the output chromatic intensity of a pixel is not determined in isolation (or looking only at the contiguous pixels) but through an operation of comparison to different local and remote areas of the image. This local/global comparison implies also a high computational cost for the algorithms: their complexity is not linear with the number of pixels;furthermore, the more systematic the comparison, the higher the complexity. Thus, most Retinex algorithms are unfit for real-time processing. The recent development of efficient Machine Learning architectures for image Processing has raised the question of whether one of the Retinex "transforms" could be efficiently learned by training a feed-forward Artificial Neural Network, thus creating a model characterized by short processing time. Selecting a variant of the Random Spray Retinex model - FuzzyRSR - as representative of the Retinex family, and choosing suitably structured autoencoder neural networks, we found that we could accurately reproduce the Retinex effects. The computational cost of the training phase was moderate, while that of the inference phases was linear in the number of pixels, and three orders of magnitude lower than the one of FuzzyRSR, thus making the ANN implementation of Retinex suitable for real-time processing.

关键词： Autoencoders FuzzyRSR image Enhancement Random Spray Retinex Retinex

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Generation of image Caption for Visually Challenged People 8th

Generation of Image Caption for Visually Challenged People

引用

8th international conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Ravi Teja, K. Sriman, Y. Aneeta Joseph, A. Deepa, R. SRM Institute of Science and Technology Vadapalani Tamil Nadu Chennai India

ISBN: (纸本)9789819748945

In recent years, advances in image interpretation and automatic image captioning have attracted lots of researchers to make use of and employ AI models. It integrates both computer vision and natural language processing (NLP) to generate descriptions in relation to the image observed. In our work, we present an assistive technology based on deep learning to better help visually challenged people to thoroughly understand images on the internet. The newly proposed automated image captioning (AIC) model consists of the following phases: data acquisition, non-captioned image selection, extraction of appearance and texture features, and generation of image captions. The model is trained to maximize the likelihood of the target description sentence to produce. Caption generation (CG) in computer vision is predicted to get a lot of interest owing to its numerous applications such as virtual assistants, image interpretation, image retrieval or indexing, and assisting visually challenged people hence improving their daily lives. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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CVulDetector: Cross-domain Vulnerability Detection based on Transfer Learning 8

CVulDetector: Cross-domain Vulnerability Detection Based on ...

引用

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

作者： Zeng, Guoqiang Zhou, Wen 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

Software vulnerabilities pose a significant threat to computer security, and accurate identification of vulnerabilities has become a challenging task. Although previous work on deep learning-based vulnerability detection has achieved some success, it still faces issues such as 1) insufficient feature extraction capability of the models, and 2) unrealistic assumption of identical distribution between the source and target domains. To address these problems, we propose CVulDetector, a cross-domain vulnerability detection scheme based on transfer learning. Our approach incorporates both natural language characteristics and semantic syntax information of programs, enabling a more comprehensive description of program features. By employing two neural network models, we extract vulnerability-rich features and fine-tune the model using limited labeled data from the target domain after pre-training on a wide range of features from the source domain. Experimental evaluation on three CWE datasets shows an F1-score of 90.6%. © 2023 IEEE.

关键词： Semantics

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Time-frequency Analysis and Convolutional Neural Network based Fuze Jamming signal Recognition 25

Time-frequency Analysis and Convolutional Neural Network Bas...

引用

25th international conference on Advanced Communication technology (ICACT) - New Cyber Security Risks for Enterprise Amidst COVID-19 Pandemic

作者： Yang, Jikai Bai, Zhiquan Hu, Jiacheng Yang, Yingchao Xian, Zhaoxia Hao, Xinhong Kwak, KyungSup Shandong Univ Sch Informat Sci & Engn Shandong Prov Key Lab Wireless Commun Technol Qingdao 266237 Shandong Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Inha Univ Grad Sch Informat Technol & Telecommun Incheon 22212 South Korea

ISBN: (纸本)9791188428106

Fuze jamming signal recognition plays a critical role in the battlefield environment. To improve the performance of fuze jamming signals detection, we propose a fuze jamming signal detector based on time-frequency analysis (TFA) and convolutional neural network (CNN), called TFA-CNN, in this paper. The detailed recognition process of the proposed TFACNN detector is provided, where the short-time Fourier transform (STFT) is first employed to convert the original jammed fuze signals into the time-frequency images and then the TFACNN detector is built to train the recognition model. Simulation results verify that the TFA-CNN detector outperforms the typical existing recognition detectors, such as LeNet, time-frequency images and convolutional neural network (TFI-CNN) and deep neural network (DNN), in the detection performance with a slightly higher time complexity. Specially, the average recognition accuracy of the proposed detector achieves 99.8% even at a low signal-to-interference-plus-noise ratio (SINR).

关键词： fuze CNN STFT image accuracy

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An Effective Hybrid image Watermarking Encrypted and Decrypted Technique for Copyright Protection 2

An Effective Hybrid Image Watermarking Encrypted and Decrypt...

引用

2nd IEEE international conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Al-Jawahry, Hassan M. Kumar, Bura Vijay Chandra, N Subhash Patil, Rajeshwari Unnisa, Raheem The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Cvr College of Engineering Telangana India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India Cmr Technical Campus Hyderabad India

ISBN: (纸本)9798350317558

image watermarking serves as a visual deterrent, signaling potential infringers that the image is protected by copyright. While various watermarking methods have been developed in the past, existing models encounter limitations such as a lack of suitability for diverse data types and ineffective data generalization. Additionally, the absence of standardization in watermarking techniques poses a significant challenge. In this proposed approach, images are input into an improved discrete Fourier transform (IDFT) to convert signals from time domain. The Arnold Transform is initially applied to convert images into a signal format. Subsequently, Walsh-Hadamard Transform (WHT) is employed for non-sinusoidal, orthogonal signal transformation. Finally, Singular Value Decomposition (SVD) is implemented to decrease number of dimensions in high-dimensional data, facilitating easier visualization and analysis. The average Peak signal-to-Noise Ratio (PSNR), Normalized Cross-Correlation (NCC), and Bit Error Rate (BER) of the hybrid IDFT-WHT-SVD watermarking scheme are reported as 56.58, 0.2651, and 0.9902, respectively. This proposed model underwent comparison with other existing models, including CNN-based robust watermarking, Hyper-Chaotically Encrypted, and Cryptographic Technique. © 2024 IEEE.

关键词： Decrypting Embedding Improved Descried Fourier Transform Singular Value Decomposition Walsh-Hadamard Transform Watermarkingg

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LimitNet: Progressive, Content-Aware image Offloading for ExtremelyWeak Devices & Networks 24

LimitNet: Progressive, Content-Aware Image Offloading for Ex...

引用

22nd Annual international conference on Mobile systems, Applications and Services (MOBISYS)

作者： Hojjat, Ali Haberer, Janek Zainab, Tayyaba Landsiedel, Olaf Univ Kiel Kiel Germany

ISBN: (纸本)9798400705816

IoT devices have limited hardware capabilities and are often deployed in remote areas. Consequently, advanced vision models surpass such devices' processing and storage capabilities, requiring offloading of such tasks to the cloud. However, remote areas often rely on LPWANs technology with limited bandwidth, high packet loss rates, and extremely low duty cycles, which makes fast offloading for time-sensitive inference challenging. Today's approaches, which are deployable on weak devices, generate a non-progressive bit stream, and therefore, their decoding quality suffers strongly when data is only partially available on the cloud at a deadline due to limited bandwidth or packet losses. In this paper, we introduce LimitNet, a progressive, content-aware image compression model designed for extremely weak devices and networks. LimitNet's lightweight progressive encoder prioritizes critical data during transmission based on the content of the image, which gives the cloud the opportunity to run inference even with partial data availability. Experimental results demonstrate that LimitNet, on average, compared to SOTA, achieves 14.01 p.p. (percentage point) higher accuracy on imageNet1000, 18.01 pp on CIFAR100, and 0.1 higher mAP@0.5 on COCO. Also, on average, LimitNet saves 61.24% bandwidth on imageNet1000, 83.68% on CIFAR100, and 42.25% on the COCO dataset compared to SOTA, while it only has 4% more encoding time compared to JPEG (with a fixed quality) on STM32F7 (Cortex-M7).

关键词： Deep Learning Edge Computing Lightweight AutoEncoders Content-Aware Encoding image Compression Progressive Offloading Progressive Compression internet of Things

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Artificial Intelligence-Powered internet of Things for Farming and Plant Research 3

Artificial Intelligence-Powered Internet of Things for Farmi...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Padma, A. Suresh, P. Narsimlu, G. Amarnath, M. Sreedhar, B.R. Chaitanya Bharathi Institute of Technology Department of Mathematics India

ISBN: (纸本)9798331509675

The wearable fiber optic sensors for plants to monitor microenvironment and growth wearable fiber optic plant sensors for microclimate and growth monitoring. The plant agricultural sectors are facing challenges because to population expansion and climate ***-powered computer vision-based plant growth monitoring system the goal of this research project is to use computer vision to create an AI-based plant growth monitoring system. The technology will allow for automatic and real-time evaluation of plant growth and health by utilizing computer vision algorithms and artificial intelligence approaches. Conventional techniques for tracking plant growth require a lot of work and time. By employing image processing techniques to analyze plant photos and extract pertinent features leaf area, plant height, and width, the suggested solution will overcome these drawbacks. Using the collected information, machine learning methods will be used to train models that can identify patterns and forecast plant development. The system will be made to adapt to various plant species and environmental circumstances. This AI-based plant growth monitoring system will help farmers, researchers, and plant enthusiasts by offering precise and timely insights into plant health and growth. This will allow them to make well-informed decisions about plant care and optimize growth conditions. By providing an economical, scalable, and effective system with numerous applications in horticulture, agriculture, and environmental research, this research project seeks to transform plant growth monitoring. © 2025 IEEE.

关键词： Smart agriculture

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Design of a Low-Cost Stochastic Computing-based Median Filter for Digital image Processing 21

Design of a Low-Cost Stochastic Computing-based Median Filte...

引用

21st international System-on-Chip Design conference, ISOCC 2024

作者： Lee, Donghui Kim, Yongtae Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of

ISBN: (纸本)9798350377088

Stochastic computing (SC) is an approximate computation method that enables the design of low-cost and error-tolerant systems. The median filter (MF) is one of the widely used filters in image processing and is particularly effective in removing salt-and-pepper noise. In this paper, we propose an energy-efficient SC-based MF. The proposed SC-based MF increases hardware efficiency while maintaining performance quality by reducing the number of comparators. The proposed SC-based MF was implemented using Verilog HDL and synthesized using 65-nm CMOS technology. The proposed SC-based MF achieves up to 63.4% and 75.2% savings in area and power, respectively, compared to the binary implementation of MF. Furthermore, compared to the conventional SC-based MF, the proposed design improves by 13.2% and 10.2%, respectively. Despite a slight decrease in peak signal-to-noise ratio (PSNR), the proposed design exhibits excellent overall hardware performance. © 2024 IEEE.

关键词： Median filters

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Research on industrial internet intrusion detection model based on optimized BI-GRU

Research on industrial internet intrusion detection model ba...

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2022 international conference on signal Processing and Communication technology, SPCT 2022

作者： Shi, Wenqiang Wang, Hao Gong, Yuanlin Xu, Hao School of automation Chongqing University of Posts and Telecommunications Chong Qing China

ISBN: (纸本)9781510664333

The intrusion detection model based on deep learning has become a research hotspot in the field of network security, aiming at the high FPR and low DR of current intrusion detection systems, this paper proposes an anomaly detection model based on deep learning. The system is first optimized by particle swarm optimization. Data feature extraction, and then through the fusion of MLP's bidirectional GRU model to better mine the information contained in the data set and its correlation, and improve the efficiency and quality of feature extraction. Finally, experiments show that the model has good performance, with a detection readiness rate of 98.74%, a false positive rate of 0.029, and a detection rate of 98.42%. The results show that the detection effect of PSO+BI-GRU+MLP is better than other algorithm models. © 2023 SPIE.

关键词： Feature extraction

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Toward Reliable Mobile CrowdSensing Data Collection: image Splicing Localization Overview 19

Toward Reliable Mobile CrowdSensing Data Collection: Image S...

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19th IEEE international Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Abou-Zbiba, Wahiba Benbrahim, Houda El Bakkali, Hanan El Gadi, Hajar Maqour, Zaina Benhaddou, Driss Mohammed V Univ IRDA ENSIAS Rabat Morocco Mohammed V Univ SSL ENSIAS Rabat Morocco Univ Houston Engn Technol Dept Houston TX 77204 USA Alfaisal Univ Dept Elect Engn Coll Engn Riyadh Saudi Arabia

ISBN: (纸本)9798350333398

With the advancement of technology, the collection of data used in Intelligent Transportation systems has become increasingly easy, notably with the emergence of the Mobile CrowdSensing paradigm. This paradigm could provide insights into traffic situation, road condition, pedestrians' behaviours, public transportation situation, and so on. Through the use of the powerful sensors in the mobile, different types of data can be generated, such as Gyroscope data, light sensors data, Magnetometer data, GPS data, Accelerometer data, videos, and images. However, the use of MCS raises issues of data reliability, as it involves the participation of several mobile owners with different 'levels' of trustworthiness. An important related issue is image forgery, i.e. the manipulation of images in order to deliberately provide misleading information, which constitutes a threat to the decision-makers of the MSC-based applications in Intelligent Transportation systems. In this paper, an overview on image forgery is presented. We examine the workflows, approaches and techniques employed for image forgery detection and localization. Then, we provide a brief review of some image splicing localization techniques. Finally, we provide a comparative analysis of handcrafted feature extraction-based techniques and deep learning-based techniques. The aim of this paper is to draw attention to the image forgery problem that could threaten any Mobile CrowdSensing application dedicated to image collection, and to provide an overview of different existing techniques that could be used to overcome this problem.

关键词： Intelligent Transportation systems Mobile CrowdSensing image Forgery image Splicing Localization

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