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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4813 条记录，以下是411-420 订阅

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Plain Source Code Obfuscation as an Effective Attack Method on IoT Malware image Classification 47

Plain Source Code Obfuscation as an Effective Attack Method ...

引用

47th ieee-computer-Society Annual international conference on computers, Software, and applications (COMPSAC)

作者： Sato, Hayato Immure, Hiroshi Ishida, Shigemi Nakamura, Yoshitaka Future Univ Hakodate Sch Syst Informat Sci Hakodate Japan Kyoto Tachibana Univ Fac Engn Kyoto Japan

ISBN: (纸本)9798350326970

IoT malware is rapidly increasing due to variants easily generated from publicly available source codes. Malware image classification capable of fast and accurate malware identification attracts attention. Since the classification by imaging is affected by malware binary changes, a binary modification without behavioral changes can be a potential attacking method to the classification by imaging. There are concerns that by combining the publicly available malware source code with readily available source code obfuscation tools, it is possible to construct an effective attack that bypasses image classifiers relatively simply. In this study, we show the effectiveness of the attack by source code obfuscation and the possibility of defense against the attack. The contribution of this research is twofold. 1) We showed that Obfuscator-LLVM (oLLVM) code obfuscation could be used as an attack method on malware image classification. The obfuscated malware binaries made by oLLVM were misclassified by VGG16-based image classifier for all the attacked malware families including Mirai, Lightaidra, and Bashlite. 2) We showed that classifier training with obfuscated samples could address this attack method. We confirmed that the malware image classifier trained with obfuscated malware binaries made by oLLVM could classify with an accuracy of 100% the malware family with obfuscation as the obfuscated original malware family.

关键词： Malware image Classification Code Obfuscation IoT Malware

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HIERARCHICAL CONDITIONAL SEMI-PAIRED image-TO-image TRANSLATION FOR MULTI-TASK image DEFECT CORRECTION ON SHOPPING WEBSITES 30

HIERARCHICAL CONDITIONAL SEMI-PAIRED IMAGE-TO-IMAGE TRANSLAT...

引用

30th ieee international conference on image processing (ICIP)

作者： Li, Moyan Fu, Jinmiao Xu, Shaoyuan Liu, Huidong Liu, Jia Wang, Bryan Univ Michigan Ann Arbor MI 48109 USA Amazon Seattle WA USA

ISBN: (纸本)9781728198354

On shopping websites, product images of low quality negatively affect customer experience. Although there are plenty of work in detecting images with different defects, few efforts have been dedicated to correct those defects at scale. A major challenge is that there are thousands of product types and each has specific defects, therefore building defect specific models is unscalable. In this paper, we propose a unified image-to-image (I2I) translation model to correct multiple defects across different product types. Our model leverages an attention mechanism which hierarchically incorporates high-level defect groups and specific defect types to guide the network to focus on defect-related image regions. Evaluated on eight public datasets, our model reduces the Frechet Inception Distance (FID) by 24.6% in average compared with MoNCE, the state-of-the-art I2I method. Another practical challenge on shopping websites is the lack of high quality paired images. We extend our model to be semi-paired by leveraging both paired and unpaired data. Tested on a shopping website dataset to correct three image defects, our model reduces (FID) by 63.2% in average compared with WS-I2I, the state-of-the art semi-paired I2I method.

关键词： image to image translation computer Vision image defect auto-correction

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Development of computerized 3D image Parametric Analysis System Based on BIM 1

Development of Computerized 3D Image Parametric Analysis Sys...

引用

1st international conference on computer Graphics and image processing, CGIP 2023

作者： Zhang, Aiguo Xu, Haiyan Nantong Institute of Technology Jiangsu Nantong China

ISBN: (纸本)9798350333183

Traditional computer 3D modelling techniques require a lot of manpower, material and time, and do not provide multiple information at the same time. BIM technology is a kind of software based on model development, and the 3D image parametric analysis system designed based on this technology can visualise design, reduce the miscommunication of information between designers and viewers, convert the data processing required in complex structured design into various kinds of graphical images that can be presented visually;check the spatial conflicts of components;simulate the construction progress, collaborate with multi-professionals;informatise project management, make it efficient, and obtain visual results quickly. It can check the spatial conflicts of components, simulate construction progress and collaborate with multi-disciplinary cooperation, inform the project management and make it efficient, and obtain visual results quickly to meet the needs of users. It also enables three-dimensional modelling functions, giving it certain rules and specifications, among other features. In addition the system uses a variety of information, which is of great value in engineering applications. © 2023 ieee.

关键词： Project management

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Improved ECA-DenseNet Framework for Brain MRI image Classification 31

Improved ECA-DenseNet Framework for Brain MRI Image Classifi...

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31st ieee conference on Signal processing and Communications applications (SIU)

作者： Aydin, Halise Nur Yildiz, Oktay Gazi Univ Bilgisayar Muhendisligi Bolumu Ankara Turkiye

ISBN: (纸本)9798350343557

Early diagnosis is very important in brain tumors. Although Magnetic Resonance (MRI) is widely used for brain tumor detection, it is difficult to detect the tumor manually. Therefore, computer-aided diagnosis systems have been frequently utilized in recent years. In this study, an Efficient Channel Attention-Dense Convolutional Network (ECA-DenseNet) framework is proposed to detect tumors in patients based on brain MRI images. While detecting the tumor, it is tried to determine which type of tumor is present in the patient. In the developed ECA-DenseNet structure, an ECA block has been added to the dense blocks. The ECA block aimed to discard unimportant information and thus reduce the computation time. The improved DenseNet model has been tested on an open-source dataset. The improved model is compared with DenseNet-121, DenseNet-169, DenseNet-201, and DenseNet-264. The experimental results show the improving model has better classification performance than the others. The accuracy of the proposed model was 95.07%.

关键词： DenseNet Efficient Channel Attention Classification SENet Brain MRId

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Research on Multi-Labels image Classification Based on Self-Supervised Model

Research on Multi-Labels Image Classification Based on Self-...

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2023 international conference on image processing and computer Vision, IPCV 2023

作者： Xu, Xuetian Guangdong Justice Police Vocational College Department of Information Administration Guang Zhou China

ISBN: (纸本)9781665473941

At present, image classification model has become an essential component for detection system. However, existing identification models are primarily concentrated on the convolutional neural network or utilize transformer model to capture the features of training images, which can obtain acceptable accuracy for single objectives and is hard to dispose multiple objectives with unsatisfied optimization results. In this work, we utilize the self-supervised mechanism to dispose the optimization process for traditional convolutional neural network. Initially, we construct the contrastive learning module to generate the amount of objectives label for the training data, which is utilized to reduce the extraction loss for multiple tasks. Subsequently, the convolutional neural network is established to classify the objectives in the images. From our extensive experimental results and compared analysis with existing classification models, we can directly observe that our proposed model can achieve the multiple images classification with more than 85% identification accuracy with reasonable responding costs. © 2023 ieee.

关键词： image classification

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Empirical Analysis of Different Existing Methods for image Enhancement in Underwater Scenarios 7

Empirical Analysis of Different Existing Methods for Image E...

引用

7th international conference on image Information processing, ICIIP 2023

作者： Vidhi Rohilla, Rajesh Delhi Technological University Department of Electronics And Communication Engineering Delhi India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371406

In recent years, there has been a lot of research on underwater image processing. Since taking images underwater is so challenging, there has been an upward trend in research in this domain. Poor contrast, blurring features, color changes, noise, and differences in the water media are common quality deterioration difficulties with underwater images. Underwater image improvement is a demanding challenge in the field of image processing with numerous real-world applications. In this study, we present an assessment of various methods used in underwater image restoration and underwater image enhancement using deep learning, including a discussion of the relevant methodologies, data sets, and assessment criteria. Moreover, a comparative analysis of various image enhancement methods with respect to accuracy has been presented. © 2023 ieee.

关键词： image enhancement

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Chambolle's Projection-based Total Variation Deepnet Layer for image Denoising 19

Chambolle's Projection-based Total Variation Deepnet Layer f...

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19th ieee international conference on Intelligent computer Communication and processing conference, ICCP 2023

作者： Grosu, George Florin Terebes, Romulus Technical University of Cluj-Napoca Communications Department Cluj-Napoca Romania

ISBN: (纸本)9798350370355

This research paper presents the implementation and evaluation of a Total Variation (TV) layer within a deep learning framework for image denoising tasks. The TV layer is based on Chambolle's projection method and aims to enhance the performance of a baseline denoising network. The study investigates the effectiveness of incorporating a single TV denoising layer, which adds only six parameters to an arbitrary baseline autoencoder. The resulting models are evaluated across three datasets with different Gaussian noise. By comparing an arbitrary network architecture with an identical counterpart model that incorporates the TV denoising layer, the results indicate significant improvements in performance. The magnitude of these performance gains diminishes as the variance of noise increases. Notably, for lower Gaussian noise variance such as for a standard deviation of 25, the gain in terms of the mean peak signal-to-noise ratio (PSNR) metric is estimated to be approximately +0.89 dB, a discernible improvement that is visually perceptible. Although the resulting model has no significant increase in terms of model size, it does present significant increases in complexity. © 2023 ieee.

关键词： image denoising

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Privacy-Preserving, Low-Storage, and High-Quality image processing in IoT and Clouds

Privacy-Preserving, Low-Storage, and High-Quality Image Proc...

引用

2023 international conference on Intelligent Communication and computer Engineering, ICICCE 2023

作者： Zhao, Shushan Chiang, Wes Central Connecticut State University Department of Computer Electronics & Graphics Technology New BritainCT United States International Business & Strategy Brock University Department of Marketing St. CatharinesON Canada

ISBN: (纸本)9798350395617

In Internet of Things (IoT) and clouds, while many image processing tasks are outsourced to third party cloud computing platforms, image processing in encrypted domain is needed in many services for data confidentiality and privacy-preserving purposes. There are many solutions for image scaling, cropping and color correction in this context. However, most of the solutions are not compatible with each other and there are few solutions supporting all these operations. In this paper, we propose a scheme that supports image scaling, cropping, and color correction in encrypted domain with minimum storage overhead. An optional improvement solution is added for a conditional quality issue in certain circumstances. Analysis shows storage requirement is drastically reduced while performance otherwise remains unchanged. © 2023 ieee.

关键词： Internet of things

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Comparison of Deep Learning Models for Automatic image Descriptors 20

Comparison of Deep Learning Models for Automatic Image Descr...

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20th ieee India Council international conference, INDICON 2023

作者： Agarwal, Lakshita Verma, Bindu Delhi Technological University Department of Information Technology Delhi India

ISBN: (纸本)9798350305593

image description is a task which combines the methods like Natural Language processing, Artificial Intelligence and computer Vision, which aims to generate contextually and semantically correct descriptions for an image dataset. The majority of the most recent methods rely on neural networks for their execution. Different Recurrent Neural Network (RNN) models are utilised to construct models for the language, and different already developed Convolutional Neural Network (CNN) models are employed for features extraction. In this paper, the main focus is on the most widely used datasets, approaches, benchmarks and the evaluation metrics for generating image descriptions. The paper involves a comparative analysis of various models for generating the descriptions and the results obtained on the Flickr8k dataset has been analyzed for them. In addition, the advantages and disadvantages of these models are being investigated. © 2023 ieee.

关键词： Automatic Learning computer Vision Deep Learning image Descriptions Natural Language processing

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Punisher: A Deep Reinforcement Learning Model Trained by Correcting Bad Actions 2

Punisher: A Deep Reinforcement Learning Model Trained by Cor...

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2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Yang, Jianyi International Technological University Computer Science Department Santa ClaraCA95050 United States

ISBN: (纸本)9798350331417

Deep Reinforcement Learning (DRL) is a deep learning (DL) network model that uses environmental feedback to train and make decisions. Expected value, as a powerful mathematical tool, is widely used in DRL network training. However, there are often deviations between the expected values and the actual values obtained from the environment. Additionally, accumulative deviations are also present in DRL networks. The accumulation of these deviations can result in a slower training speed and negatively impact the network's stability. To address these issues, this paper proposes a new DRL training method called Punisher. The principle behind Punisher is to identify the bad actions made by the DRL agent and correct only those actions during the training process. By focusing on correcting the bad actions, Punisher aims to improve the overall performance and stability of the DRL network. The experimental results demonstrate that the Punisher method exhibits excellent performance, faster training speeds and greater network stability, making it a promising approach for efficiently training DRL agents in various applications. Experiments presented in the main content of this paper have been posted at GitHub at https://***/Jimmyoungyi/Punisher. © 2023 ieee.

关键词： deep learning punisher reinforcement learning

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