Pub Date : 2022-05-20DOI: 10.1109/cvidliccea56201.2022.9824311
Shiwen Li
The field of computer image generation is developing rapidly, and more and more personalized image-to-image style transfer software is produced. Image translation can convert two different styles of data to generate realistic pictures, which can not only meet the individual needs of users, but also meet the problem of insufficient data for a certain style of pictures. Transformers not only have always occupied an important position in the NLP field. In recent years, due to its model interpretability and strong multimodal fusion ability, it has also performed well in the field of computer vision. This paper studies the application of Transformers in the field of image-to-image style transfer. Replace the traditional CNN structure with the improved Transformer of the discriminator and generator model of CycleGAN, and a comparative experiment is carried out with the traditional CycleGAN. The test dataset uses the public datasets Maps and CelebA, and the results are comparable to those of the traditional CycleGAN. This paper shows that Transformer can perform the task of image-to-image style transfer on unsupervised GAN, which expands the application of Transformer in the CV filed, and can be used as a general architecture applied to more vision tasks in the future.
{"title":"Trans-CycleGAN: Image-to-Image Style Transfer with Transformer-based Unsupervised GAN","authors":"Shiwen Li","doi":"10.1109/cvidliccea56201.2022.9824311","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824311","url":null,"abstract":"The field of computer image generation is developing rapidly, and more and more personalized image-to-image style transfer software is produced. Image translation can convert two different styles of data to generate realistic pictures, which can not only meet the individual needs of users, but also meet the problem of insufficient data for a certain style of pictures. Transformers not only have always occupied an important position in the NLP field. In recent years, due to its model interpretability and strong multimodal fusion ability, it has also performed well in the field of computer vision. This paper studies the application of Transformers in the field of image-to-image style transfer. Replace the traditional CNN structure with the improved Transformer of the discriminator and generator model of CycleGAN, and a comparative experiment is carried out with the traditional CycleGAN. The test dataset uses the public datasets Maps and CelebA, and the results are comparable to those of the traditional CycleGAN. This paper shows that Transformer can perform the task of image-to-image style transfer on unsupervised GAN, which expands the application of Transformer in the CV filed, and can be used as a general architecture applied to more vision tasks in the future.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"178 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72704033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-20DOI: 10.1109/cvidliccea56201.2022.9824844
Yuanyuan Tian, Hongyue You, Zhongying Li, Haixiang Nie, Xiang Ren
In this paper, Abaqus finite element software is used to calculate the nonlinear deformation value of steel-plastic grid and building membrane structure under the action of uniform load, which lays the foundation for similar engineering applications and software calculation in the future. The specific conclusions are as follows: 1) With the continuous increase of the uniform load, the displacement change of the steel-plastic grid is always greater than the displacement change of the building membrane structure, and the nonlinear change curve trends of the two are consistent. 2) Both showed that the deformation value of the center point range showed a nonlinear growth trend with the increase of the load. 3) With the continuous increase of the applied uniformly distributed load, the rate of deformation increase is also significantly increased.
{"title":"Analysis of nonlinear deformation relationship between steel-plastic grid and membrane structure based on Abaqus software","authors":"Yuanyuan Tian, Hongyue You, Zhongying Li, Haixiang Nie, Xiang Ren","doi":"10.1109/cvidliccea56201.2022.9824844","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824844","url":null,"abstract":"In this paper, Abaqus finite element software is used to calculate the nonlinear deformation value of steel-plastic grid and building membrane structure under the action of uniform load, which lays the foundation for similar engineering applications and software calculation in the future. The specific conclusions are as follows: 1) With the continuous increase of the uniform load, the displacement change of the steel-plastic grid is always greater than the displacement change of the building membrane structure, and the nonlinear change curve trends of the two are consistent. 2) Both showed that the deformation value of the center point range showed a nonlinear growth trend with the increase of the load. 3) With the continuous increase of the applied uniformly distributed load, the rate of deformation increase is also significantly increased.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"1 1","pages":"1075-1078"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76125913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-20DOI: 10.1109/cvidliccea56201.2022.9824815
Guanjing Li
In recent years, the detection and recognition of scene text have developed rapidly, but two difficult challenges have not been well solved. First, semantic analysis based on convolutional neural networks and powerful ImageNet pre-training incur high computational costs. Second, scene text detection with irregular shapes and irregular word order is inaccurate. Aiming at the above problems, this paper proposes a novel and lightweight network module (CSNet-PGNet) for real-time reading of a text of arbitrary shape and orientation. CSNet (Cross-Stage Cross-Scale network) is an extremely lightweight overall cross-stage and cross-scale network, which abandons the cumbersome CNN skeleton network (semantic classification) and can be trained from scratch. PGNet (Point Gathering Network) is a text detection recognizer that can detect and recognize the text of any shape, without the operation of Non-maximum Suppression (NMS) and Region of Interest (RoI), and has the advantages of end-to-end simplicity and efficiency. performance. This paper proposes the CSNet-PGNet scene curve text detection and recognition method, which is a development to more efficient and precise scene text detection of any shapes.
{"title":"CSNet-PGNet: Algorithm for Scene Text Detection and Recognition","authors":"Guanjing Li","doi":"10.1109/cvidliccea56201.2022.9824815","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824815","url":null,"abstract":"In recent years, the detection and recognition of scene text have developed rapidly, but two difficult challenges have not been well solved. First, semantic analysis based on convolutional neural networks and powerful ImageNet pre-training incur high computational costs. Second, scene text detection with irregular shapes and irregular word order is inaccurate. Aiming at the above problems, this paper proposes a novel and lightweight network module (CSNet-PGNet) for real-time reading of a text of arbitrary shape and orientation. CSNet (Cross-Stage Cross-Scale network) is an extremely lightweight overall cross-stage and cross-scale network, which abandons the cumbersome CNN skeleton network (semantic classification) and can be trained from scratch. PGNet (Point Gathering Network) is a text detection recognizer that can detect and recognize the text of any shape, without the operation of Non-maximum Suppression (NMS) and Region of Interest (RoI), and has the advantages of end-to-end simplicity and efficiency. performance. This paper proposes the CSNet-PGNet scene curve text detection and recognition method, which is a development to more efficient and precise scene text detection of any shapes.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"3 1","pages":"1217-1224"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75119597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-20DOI: 10.1109/cvidliccea56201.2022.9825217
Yongliang Wang, Xiaoliang Guo
Cell counting is a basic and important detection technique in biomedical diagnosis. However, current cell counting devices are expensive and bulky, which are not conducive to rapid cell counting. To solve this problem, we design a low-cost and simple on-chip cell counting device based on lensless imaging technology. The device uses an ordinary white LED light and a CMOS image sensor to capture the cell image in the microfluidic chip, and uses a microcomputer - Raspberry Pi 4B for image transmission and processing. Then a self-developed processing program is designed to count the cells. In addition, the device can perform micron-scale particle imaging, which can identify microbeads of different sizes. Compared with other lensless imaging devices, our device has obvious advantages in low cost, scalability, and degree of automation, which can improve the efficiency of biological experiments, and is of great significance for expanding the population of healthcare services in the future.
{"title":"A low-cost and simple on-chip cell counting device based on lensless imaging technology","authors":"Yongliang Wang, Xiaoliang Guo","doi":"10.1109/cvidliccea56201.2022.9825217","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825217","url":null,"abstract":"Cell counting is a basic and important detection technique in biomedical diagnosis. However, current cell counting devices are expensive and bulky, which are not conducive to rapid cell counting. To solve this problem, we design a low-cost and simple on-chip cell counting device based on lensless imaging technology. The device uses an ordinary white LED light and a CMOS image sensor to capture the cell image in the microfluidic chip, and uses a microcomputer - Raspberry Pi 4B for image transmission and processing. Then a self-developed processing program is designed to count the cells. In addition, the device can perform micron-scale particle imaging, which can identify microbeads of different sizes. Compared with other lensless imaging devices, our device has obvious advantages in low cost, scalability, and degree of automation, which can improve the efficiency of biological experiments, and is of great significance for expanding the population of healthcare services in the future.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"41 1","pages":"559-562"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77885398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-20DOI: 10.1109/cvidliccea56201.2022.9824787
Chen Shi
This paper use pointer networks to improve the quality of initial solutions generation to solve slow convergence problems and the tendency to fall into local optimal solutions when solving path planning problems by the heuristic algorithm. The results show that the convergence speed and the optimisation outcome of the optimised algorithm are improved and can be effectively used to improve the application of heuristic algorithms such as VNS to the travelling salesman problem.
{"title":"Pointer Network Solution Pool : Combining Pointer Networks and Heuristics to Solve TSP Problems","authors":"Chen Shi","doi":"10.1109/cvidliccea56201.2022.9824787","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824787","url":null,"abstract":"This paper use pointer networks to improve the quality of initial solutions generation to solve slow convergence problems and the tendency to fall into local optimal solutions when solving path planning problems by the heuristic algorithm. The results show that the convergence speed and the optimisation outcome of the optimised algorithm are improved and can be effectively used to improve the application of heuristic algorithms such as VNS to the travelling salesman problem.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"77 1","pages":"1236-1242"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80211902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-20DOI: 10.1109/cvidliccea56201.2022.9824208
Caixia Ma, Lei Lyu, Chen Lyu
Effective detection of shot boundaries is important for video summarization methods based on shot boundary detection. However, various gradual shot boundaries (such as, fade in, fade out, dissolve) pose a great challenge to shot boundary detection. Previous work constructs graph models based on feature histograms from images and analyzes the structural changes of graphs, thus improving the detection of gradual shots. In this paper, we develop a new quantization method to calculate the structural change of the graph so as to more accurately locate the gradual shot boundaries. Statistical analysis methods are performed to analyze the data to be detected with past data to achieve real-time shot boundary detection. Experimental results on the VSUMM dataset show that our method outperforms some state-of-the-art methods on the F-Score.
{"title":"A Novel Graph-Based Structural Dissimilarity Measure for Video Summarization","authors":"Caixia Ma, Lei Lyu, Chen Lyu","doi":"10.1109/cvidliccea56201.2022.9824208","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824208","url":null,"abstract":"Effective detection of shot boundaries is important for video summarization methods based on shot boundary detection. However, various gradual shot boundaries (such as, fade in, fade out, dissolve) pose a great challenge to shot boundary detection. Previous work constructs graph models based on feature histograms from images and analyzes the structural changes of graphs, thus improving the detection of gradual shots. In this paper, we develop a new quantization method to calculate the structural change of the graph so as to more accurately locate the gradual shot boundaries. Statistical analysis methods are performed to analyze the data to be detected with past data to achieve real-time shot boundary detection. Experimental results on the VSUMM dataset show that our method outperforms some state-of-the-art methods on the F-Score.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"19 1","pages":"643-647"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85150611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-20DOI: 10.1109/cvidliccea56201.2022.9825283
G. Gan, Xu Xiao, Chuntao Jiang, Yingxi Ye, Yuhao He, Yushen Xu, Chunhai Luo
Strawberry disease and pest identification and control were rarely studied, with few high-quality open image datasets to date. In view of this situation, firstly, the images of common strawberry pests and diseases of 13 categories were collected both online and offline independently to be constructed into datasets. Secondly, the SE-ResNeXt50 model was created, which had better usability than the residual network model ResNet50. To be specific, the Inception was combined with the ResNet50 model to widen the network, 32 branches were set, and the attention mechanism, the squeeze and excitation module (SE), was also imported, which solved the problems of the complex image background and information interference and improved the identification efficiency and accuracy of the model. The results showed that the accuracy of the SE-ResNeXt50 model, reaching 89.3%, was 8% higher than that of the ResNet50 model. The SEResNeXt50 model had plateaued after iterating 15 times, indicating its good identification performance. Besides, the SEResNeXt50 model, which was developed based on the data obtained in real life, had good generalization ability and robustness, better meeting the demands of strawberry growers. A WeChat mini-program for strawberry disease and pest identification based on the SE-ResNeXt50 model was developed, enabling the fruit growers to identify the strawberry pests and diseases easily and get prevention suggestions, promoting the development of the strawberry industry.
{"title":"Strawberry Disease and Pest Identification and Control Based on SE-ResNeXt50 Model","authors":"G. Gan, Xu Xiao, Chuntao Jiang, Yingxi Ye, Yuhao He, Yushen Xu, Chunhai Luo","doi":"10.1109/cvidliccea56201.2022.9825283","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825283","url":null,"abstract":"Strawberry disease and pest identification and control were rarely studied, with few high-quality open image datasets to date. In view of this situation, firstly, the images of common strawberry pests and diseases of 13 categories were collected both online and offline independently to be constructed into datasets. Secondly, the SE-ResNeXt50 model was created, which had better usability than the residual network model ResNet50. To be specific, the Inception was combined with the ResNet50 model to widen the network, 32 branches were set, and the attention mechanism, the squeeze and excitation module (SE), was also imported, which solved the problems of the complex image background and information interference and improved the identification efficiency and accuracy of the model. The results showed that the accuracy of the SE-ResNeXt50 model, reaching 89.3%, was 8% higher than that of the ResNet50 model. The SEResNeXt50 model had plateaued after iterating 15 times, indicating its good identification performance. Besides, the SEResNeXt50 model, which was developed based on the data obtained in real life, had good generalization ability and robustness, better meeting the demands of strawberry growers. A WeChat mini-program for strawberry disease and pest identification based on the SE-ResNeXt50 model was developed, enabling the fruit growers to identify the strawberry pests and diseases easily and get prevention suggestions, promoting the development of the strawberry industry.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"59 1","pages":"237-243"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86047177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-20DOI: 10.1109/cvidliccea56201.2022.9824276
Shuai Zhang, Xiuqing Mao, Lei Sun, Yu Yang
Because EEG-based identity requires a large amount of training data when training a classification model, and the collection of EEG signals requires a lot of time and effort. Therefore, we hope to perform data augmentation on the EEG data used for identity. Generative adversarial networks have achieved great success in image generation, but the raw EEG signals are not in the form of images. Therefore, we process the EEG signal into an EEG topomap with stronger spatial feature representation, and use a spatial feature-based generative adversarial network image augmentation method (SF-GAN). To verify the generality of our proposed method, we use real EEG topomap samples processed from two different EEG datasets, BCI Competition IV 1 and BCI Competition IV 2a, to train SF-GAN to generate augmented samples for training identity classification model. The proposed method can use smaller real samples to expand the training set of identity, reduce the data dependence on real samples, and reduce the time of data collection to a certain extent. And it is proved by experiments that the data generated by this method can further improve the training effect of the classification model.
因为基于脑电信号的识别在训练分类模型时需要大量的训练数据，而脑电信号的采集需要耗费大量的时间和精力。因此，我们希望对用于身份识别的EEG数据进行数据增强。生成对抗网络在图像生成方面取得了很大的成功，但是原始的脑电信号并不是图像的形式。因此，我们将脑电信号处理成具有更强空间特征表示的脑电信号地形图，并采用基于空间特征的生成式对抗网络图像增强方法(SF-GAN)。为了验证所提方法的泛化性，我们使用两个不同脑电数据集(BCI Competition IV 1和BCI Competition IV 2a)处理的真实脑电地形图样本来训练SF-GAN，生成增强样本用于训练身份分类模型。所提出的方法可以使用较小的真实样本来扩展身份的训练集，减少数据对真实样本的依赖，并在一定程度上减少数据收集的时间。并且通过实验证明，该方法生成的数据可以进一步提高分类模型的训练效果。
{"title":"EEG data augmentation for Personal Identification Using SF-GAN","authors":"Shuai Zhang, Xiuqing Mao, Lei Sun, Yu Yang","doi":"10.1109/cvidliccea56201.2022.9824276","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824276","url":null,"abstract":"Because EEG-based identity requires a large amount of training data when training a classification model, and the collection of EEG signals requires a lot of time and effort. Therefore, we hope to perform data augmentation on the EEG data used for identity. Generative adversarial networks have achieved great success in image generation, but the raw EEG signals are not in the form of images. Therefore, we process the EEG signal into an EEG topomap with stronger spatial feature representation, and use a spatial feature-based generative adversarial network image augmentation method (SF-GAN). To verify the generality of our proposed method, we use real EEG topomap samples processed from two different EEG datasets, BCI Competition IV 1 and BCI Competition IV 2a, to train SF-GAN to generate augmented samples for training identity classification model. The proposed method can use smaller real samples to expand the training set of identity, reduce the data dependence on real samples, and reduce the time of data collection to a certain extent. And it is proved by experiments that the data generated by this method can further improve the training effect of the classification model.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"6 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77065359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Harsh working environment and living conditions lead workers to become hawkers, and to manage the vendor economy, a significant number of Street Vendor Economic Governance Models have appeared. These models have a particular influence on the operational benefit of the vendor economy. This paper studies the influence of multi-scale network technology regulation mechanisms and incentive mechanisms on the stallholder economy. A model based on the OFRTB network is proposed. The model accurately simulates the transaction process in a multi-scale network flow by adding a new state of supervision node. Finally, we discuss and analyze each state’s transition process and probability in the OFRTB model through experimental simulation. This study is helpful to improve the management of mobile vendors further and improve the efficiency of government services.
{"title":"Research on Multi-scale Network Computer Modeling based on Mobile Vendor Management","authors":"Yufan Yang, Xingchen Dong, Yingjun Li, Huaiyang Zhang","doi":"10.1109/cvidliccea56201.2022.9824717","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9824717","url":null,"abstract":"Harsh working environment and living conditions lead workers to become hawkers, and to manage the vendor economy, a significant number of Street Vendor Economic Governance Models have appeared. These models have a particular influence on the operational benefit of the vendor economy. This paper studies the influence of multi-scale network technology regulation mechanisms and incentive mechanisms on the stallholder economy. A model based on the OFRTB network is proposed. The model accurately simulates the transaction process in a multi-scale network flow by adding a new state of supervision node. Finally, we discuss and analyze each state’s transition process and probability in the OFRTB model through experimental simulation. This study is helpful to improve the management of mobile vendors further and improve the efficiency of government services.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"29 1","pages":"61-64"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78892476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-20DOI: 10.1109/cvidliccea56201.2022.9825448
Peiyu Lu, Chao Feng, Chaojing Tang
In cause analysis of vulnerability, multi-level dereference of pointer and array element index analysis are often encountered at the code level, which is reflected in the case of indirect addressing at the assembly level. At present, the program slicing technology commonly used for cause analysis of vulnerability can not completely analyze the data flow and control flow of indirect addressing. In order to solve this problem, this paper proposes a binary program dynamic slicing technology for cause analysis of vulnerability. This technology uses the information related to the reading and writing of registers and memory addresses in the program execution trace to find the relationship of the data flow and control flow between the two instructions, which can more completely retain the information related to the instructions to be sliced, improve the automation component in cause analysis of vulnerability and reduce the cost of manual analysis. In addition, using the static characteristics of execution trace, this paper can meet the needs of researchers for repeated debugging and analysis of a program execution at different time points in the process of program execution.
{"title":"Research on Binary Program Dynamic Slicing Technology for Cause Analysis of Vulnerability","authors":"Peiyu Lu, Chao Feng, Chaojing Tang","doi":"10.1109/cvidliccea56201.2022.9825448","DOIUrl":"https://doi.org/10.1109/cvidliccea56201.2022.9825448","url":null,"abstract":"In cause analysis of vulnerability, multi-level dereference of pointer and array element index analysis are often encountered at the code level, which is reflected in the case of indirect addressing at the assembly level. At present, the program slicing technology commonly used for cause analysis of vulnerability can not completely analyze the data flow and control flow of indirect addressing. In order to solve this problem, this paper proposes a binary program dynamic slicing technology for cause analysis of vulnerability. This technology uses the information related to the reading and writing of registers and memory addresses in the program execution trace to find the relationship of the data flow and control flow between the two instructions, which can more completely retain the information related to the instructions to be sliced, improve the automation component in cause analysis of vulnerability and reduce the cost of manual analysis. In addition, using the static characteristics of execution trace, this paper can meet the needs of researchers for repeated debugging and analysis of a program execution at different time points in the process of program execution.","PeriodicalId":23649,"journal":{"name":"Vision","volume":"4 1","pages":"783-788"},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87729741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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