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A Dual-Chaining Watermark Scheme for Data Integrity Protection in Internet of Things

Computers, Materials & Continua 2019年第3期58卷 679-695页

作者： Baowei Wang Weiwen Kong Wei Li neal nxiong Jiangsu Engineering Centre of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologySchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Electronic Information and Control from Fujian Provincial Education Department Fuzhou350108China Department of Mathematics and Computer Science Northeastern State UniversityOklahomaUSA.

Chaining watermark is an effective way to verify the integrity of streaming data in wireless network environment,especially in resource-constrained sensor networks,such as the perception layer of Internet of Things ***,in all existing single chaining watermark schemes,how to ensure the synchronization between the data sender and the receiver is still an unsolved *** the synchronization points are attacked by the adversary,existing data integrity authentication schemes are difficult to work properly,and the false negative rate might be up to 50 *** the additional fixed group delimiters not only increase the data size,but are also easily detected by *** this paper,we propose an effective dual-chaining watermark scheme,called DCW,for data integrity protection in smart campus IoT *** proposed DCW scheme has the following three characteristics:(1)In order to authenticate the integrity of the data,fragile watermarks are generated and embedded into the data in a chaining way using dynamic grouping;(2)Instead of additional fixed group delimiters,chained watermark delimiters are proposed to synchronize the both transmission sides in case of the synchronization points are tampered;(3)To achieve lossless integrity authentication,a reversible watermarking technique is *** experimental results and security analysis can prove that the proposed DCW scheme is able to effectively authenticate the integrity of the data with free distortion at low cost in our smart meteorological Internet of Things system.

关键词： Dual-chaining reversible watermark integrity authentication Internet of things.

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Algorithm of Helmet Wearing Detection Based on AT-YOLO Deep Mode

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Computers, Materials & Continua 2021年第10期69卷 159-174页

作者： Qingyang Zhou Jiaohua Qin Xuyu Xiang Yun Tan neal nxiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464OKUSA

The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements,but they can’t accurately detect small objects and objects with ***,we propose a helmet detection algorithm based on the attention mechanism(AT-YOLO).First of all,a channel attention module is added to the YOLOv3 backbone network,which can adaptively calibrate the channel features of the direction to improve the feature utilization,and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the feature map so that to increase the receptive field of the ***,we use DIoU(Distance Intersection over Union)bounding box regression loss function,it not only improving the measurement of bounding box regression loss but also increases the normalized distance loss between the prediction boxes and the target boxes,which makes the network more accurate in detecting small objects and faster in ***,we explore the training strategy of the network model,which improves network performance without increasing the inference *** show that the mAP of the proposed method reaches 96.5%,and the detection speed can reach 27 *** with other existing methods,it has better performance in detection accuracy and speed.

关键词： Safety helmet detection attention mechanism convolutional neural network training strategies

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Coverless Image Steganography Based on Image Segmentation

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Computers, Materials & Continua 2020年第8期64卷 1281-1295页

作者： Yuanjing Luo Jiaohua Qin Xuyu Xiang Yun Tan Zhibin He neal nxiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410114China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA

To resist the risk of the stego-image being maliciously altered during transmission,we propose a coverless image steganography method based on image *** existing coverless steganography methods are based on whole feature mapping,which has poor robustness when facing geometric attacks,because the contents in the image are easy to *** solve this problem,we use ResNet to extract semantic features,and segment the object areas from the image through Mask RCNN for information *** selected object areas have ethical structural integrity and are not located in the visual center of the image,reducing the information loss of malicious ***,these object areas will be binarized to generate hash sequences for information *** transmission,only a set of stego-images unrelated to the secret information are transmitted,so it can fundamentally resist *** the same time,since both Mask RCNN and ResNet have excellent robustness,pre-training the model through supervised learning can achieve good *** robust hash algorithm can also resist attacks during *** image segmentation will reduce the capacity,multiple object areas can be extracted from an image to ensure the capacity to a certain *** results show that compared with other coverless image steganography methods,our method is more robust when facing geometric attacks.

关键词： Coverless steganography semantic feature image segmentation Mask RCNN ResNet

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Ecological Security Evaluation Algorithm for Resource-Exhausted Cities Based on the PSR Model

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Computers, Materials & Continua 2021年第10期69卷 985-1001页

作者： Xiaozhou Li Zhenggang Xu Yuanyuan Fu Qi Jin Yunlin Zhao neal nxiong Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security for Hunan Province Central South University of Forestry and TechnologyChangsha410004China Hunan Urban and Rural Ecological Planning and Restoration Engineering Research Center Hunan City UniversityYiyang413000China College of Information and Electronic Engineering Hunan City UniversityYiyang413000China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA

Today,resource depletion threatens a number of resource-based cities in *** ecological security problem caused by the long-term exploitation of natural resources is a key issue to be solved in the development of resource-exhausted *** 23 indicators,this study evaluated the ecological security status and development trends of 21 resource-exhausted cities in China from 2011 to *** results showed that from 2011 to 2015,the overall ecological security of this type of city was low,with over 60%of the cities at an unsafe ***,ecological security improved rapidly after 2016,and by 2017,all of the cities had reached the critical safety *** top 10 indicators of ecological security included industrial sulfur dioxide emissions,water supply,agricultural fertilizer application,and forest *** 10 indicators’cumulative contribution to ecological security was 48.3%;among them,reducing industrial sulfur dioxide emissions contributed the most at 5.7%.These findings can help governments better understand the ecological security status of resource-exhausted cities,and it can provide a reference for the allocation of funds and other resources to improve the ecological safety of these cities.

关键词： Ecological security PSR model evaluation resource-exhausted cities

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Adaptive Median Filtering Algorithm Based on Divide and Conquer and Its Application in CAPTCHA Recognition

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Computers, Materials & Continua 2019年第3期58卷 665-677页

作者： Wentao Ma Jiaohua Qin Xuyu Xiang Yun Tan Yuanjing Luo neal nxiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410114China Department of Mathematics and Computer Science Northeastern State UniversityOklahoma74464USA.

As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious *** researching the CAPTCHA,we can find its vulnerability and improve the security of ***,many studies have shown that improving the image preprocessing effect of the CAPTCHA,which can achieve a better recognition rate by the state-of-theart machine learning *** are many kinds of noise and distortion in the CAPTCHA images of this *** propose an adaptive median filtering algorithm based on divide and conquer in this ***,the filtering window data quickly sorted by the data correlation,which can greatly improve the filtering ***,the size of the filtering window is adaptively adjusted according to the noise *** demonstrated in the experimental results,the proposed scheme can achieve superior performance compared with the conventional median *** algorithm can not only effectively detect the noise and remove it,but also has a good effect in preservation ***,this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications.

关键词： Image preprocessing machine learning CAPTCHA recognition adaptive median filtering algorithm.

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Privacy Protection for Medical Images Based on DenseNet and Coverless Steganography

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Computers, Materials & Continua 2020年第9期64卷 1797-1817页

作者： Yun Tan Jiaohua Qin Hao Tang Xuyu Xiang Ling Tan neal nxiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410114China The Second Xiangya Hospital of Central South University Changsha410011China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464USA

With the development of the internet of medical things(IoMT),the privacy protection problem has become more and more *** this paper,we propose a privacy protection scheme for medical images based on DenseNet and coverless *** a given group of medical images of one patient,DenseNet is used to regroup the images based on feature similarity *** the mapping indexes can be constructed based on LBP feature and hash *** mapping the privacy information with the hash sequences,the corresponding mapped indexes of secret information will be packed together with the medical images group and released to the authorized *** user can extract the privacy information successfully with a similar method of feature analysis and index *** simulation results show good performance of *** the hiding success rate also shows good feasibility and practicability for *** the medical images are kept original without embedding and modification,the performance of crack resistance is outstanding and can keep better quality for diagnosis compared with traditional schemes with data embedding.

关键词： Privacy protection medical image coverless steganography DenseNet LBP

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