scholarly journals A Framework for Leveraging Image Security in Cloud with Simultaneous Compression and Encryption Using Compressive Sensing

2021 ◽  
Vol 35 (1) ◽  
pp. 85-91
Author(s):  
Naga Raju Hari Manikyam ◽  
Munisamy Shyamala Devi
Keyword(s):  
Compressive Sensing ◽  
Image Data ◽  
Input Image ◽  
Security And Privacy ◽  
Cloud Infrastructure ◽  
Security Framework ◽  
Image Security ◽  
Compression Performance ◽  
Security Algorithm ◽  
Simultaneous Sensing

In the contemporary era, technological innovations like cloud computing and Internet of Things (IoT) pave way for diversified applications producing multimedia content. Especially large volumes of image data, in medical and other domains, are produced. Cloud infrastructure is widely used to reap benefits such as scalability and availability. However, security and privacy of imagery is in jeopardy when outsourced it to cloud directly. Many compression and encryption techniques came into existence to improve performance and security. Nevertheless, in the wake of emergence of quantum computing in future, there is need for more secure means with multiple transformations of data. Compressive sensing (CS) used in existing methods to improve security. However, most of the schemes suffer from the problem of inability to perform compression and encryption simultaneously besides ending up with large key size. In this paper, we proposed a framework known as Cloud Image Security Framework (CISF) leveraging outsourced image security. The framework has an underlying algorithm known as Hybrid Image Security Algorithm (HISA). It is based on compressive sensing, simultaneous sensing and encryption besides random pixel exchange to ensure multiple transformations of input image. The empirical study revealed that the CISF is more effective, secure with acceptable compression performance over the state of the art methods.

scholarly journals Efficient Lossless Compression of Multitemporal Hyperspectral Image Data

2018 ◽  
Vol 4 (12) ◽  
pp. 142 ◽  
Author(s):  
Hongda Shen ◽  
Zhuocheng Jiang ◽  
W. Pan
Keyword(s):  
Data Compression ◽  
Hyperspectral Image ◽  
Image Data ◽  
Lossless Compression ◽  
Large Data ◽  
Hyperspectral Data ◽  
Compression Performance ◽  
Temporal Correlations ◽  
Sensing Applications ◽  
Data Volume

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

scholarly journals MRI Image Reconstruction using Compressive Sensing

2019 ◽  
Vol 8 (2) ◽  
pp. 5256-5260
Keyword(s):  
Compressive Sensing ◽  
Communication Systems ◽  
Optimization Algorithms ◽  
Image Data ◽  
Acquisition Time ◽  
Huge Amount ◽  
Scan Time ◽  
Small Set ◽  
Picture Archiving And Communication ◽  
Made In

A large number of diagnostic images which also include the MRIs are generated by the imaging departments of the hospitals for medical and legal reasons. This results in the creation of a huge amount of data in the form of images which are required to be stored for a long period. The primary challenge for the picture archiving and communication systems (PACS) allowing to store the image data and the display and reconstruction of the image for recalling at various sites. Image compression and reconstruction are necessary to cope up with these tasks. Significant efforts have been made in the recent towards the application of compressive sensing techniques for acquiring the data in MRI process. The primary aim of the theory of Compressive Sensing (CS) in signal processing is reducing the quantity of data that is acquired for successfully reconstructing the signals. Decreasing the number of coefficients of the acquired images will result in reduced acquisition time i.e. nothing but the duration for which the images are exposed to the MRI apparatus. This paper aims at using optimization algorithms in designing the scanner of the MR integrated with the CS, which results in the reduction of the scan time of the MRI. From a small set of acquired samples, images of satisfactory quality can be obtained. Various Compressive Sensing based optimization algorithms for reconstructing the MRI images are assessed, and a relative comparison is done for further research in this paper.

Efficient Cyber Security Framework for Smart Cities

2019 ◽  
pp. 130-157 ◽  
Author(s):  
Amtul Waheed ◽  
Jana Shafi
Keyword(s):  
Cyber Security ◽  
Smart City ◽  
Smart Cities ◽  
Cost Effective ◽  
Cyber Attacks ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Security Framework ◽  
Smart Transportation ◽  
Security Issues

Smart cities are established on some smart components such as smart governances, smart economy, science and technology, smart politics, smart transportation, and smart life. Each and every smart object is interconnected through the internet, challenging the security and privacy of citizen's sensitive information. A secure framework for smart cities is the only solution for better and smart living. This can be achieved through IoT infrastructure and cloud computing. The combination of IoT and Cloud also increases the storage capacity and computational power and make services pervasive, cost-effective, and accessed from anywhere and any device. This chapter will discuss security issues and challenges of smart city along with cyber security framework and architecture of smart cities for smart infrastructures and smart applications. It also presents a general study about security mechanism for smart city applications and security protection methodology using IOT service to stand against cyber-attacks.

Experiment of OCITN: Considering Appropriate Goal Images and Metric for One-Class Image Transformation Network

2021 ◽  
Author(s):  
Toshitaka Hayashi ◽  
Hamido Fujita
Keyword(s):  
Image Data ◽  
Classification Problem ◽  
Model Error ◽  
Input Image ◽  
Training Data ◽  
Distance Metric ◽  
Image Transformation ◽  
Image Entropy ◽  
Image Metrics ◽  
One Class Classification

One-class classification (OCC) is a classification problem where training data includes only one class. In such a problem, two types of classes exist, seen class and unseen class, and classifying these classes is a challenge. Besides, One-class Image Transformation Network (OCITN) is an OCC algorithm for image data. In which, image transformation network (ITN) is trained. ITN aims to transform all input image into one image, namely goal image. Moreover, the model error of ITN is computed as a distance metric between ITN output and a goal image. Besides, OCITN accuracy is related to goal image, and finding an appropriate goal image is challenging. In this paper, 234 goal images are experimented with in OCITN using the CIFAR10 dataset. Experiment results are analyzed with three image metrics: image entropy, similarity with seen images, and image derivatives.

Spectral Residual Based Underwater Animal Detection

2013 ◽  
Vol 850-851 ◽  
pp. 970-973
Author(s):  
Zhe Chen ◽  
Jian Qiang Gao ◽  
Jie Shen ◽  
Hui Bin Wang
Keyword(s):  
Statistical Analysis ◽  
Frequency Domain ◽  
Image Data ◽  
Input Image ◽  
Monitor System ◽  
Frequency Curve ◽  
Underwater Image ◽  
Spectral Residual ◽  
Animal Detection ◽  
Spatial Frequency Domain

In this paper, the spectral residual method is applied in the underwater image data for detecting the animals. The system is designed to assist the underwater monitor system survey operations, specialized to the task of animal detection. Firstly, the regularity for the frequency spectrum of the images collected in the underwater world is discovered by the statistical analysis. Then we transform the input image into the spatial frequency domain and singularities including in the frequency curve is extracted by average filtering. Finally, these singularities are inverse transformed from the frequency domain into spatial domain and the saliency area is detected. Experimental results, which have been performed on a set of real underwater images acquired in different environments, demonstrate the robustness and the accuracy of the proposed system in the task of underwater animal detection.

scholarly journals From the slit-island method to the Ising model: Analysis of irregular grayscale objects

2014 ◽  
Vol 24 (1) ◽  
pp. 49-63 ◽  
Author(s):  
Przemysław Mazurek ◽  
Dorota Oszutowsk A-M Ażurek
Keyword(s):  
Image Analysis ◽  
Fractal Dimension ◽  
Ising Model ◽  
Image Data ◽  
Input Image ◽  
Cell Nuclei ◽  
Cervical Cell ◽  
Atypical Cells ◽  
Model Context ◽  
Slit Island Method

Abstract The Slit Island Method (SIM) is a technique for the estimation of the fractal dimension of an object by determining the area- perimeter relations for successive slits. The SIM could be applied for image analysis of irregular grayscale objects and their classification using the fractal dimension. It is known that this technique is not functional in some cases. It is emphasized in this paper that for specific objects a negative or an infinite fractal dimension could be obtained. The transformation of the input image data from unipolar to bipolar gives a possibility of reformulated image analysis using the Ising model context. The polynomial approximation of the obtained area-perimeter curve allows object classification. The proposed technique is applied to the images of cervical cell nuclei (Papanicolaou smears) for the preclassification of the correct and atypical cells.

scholarly journals Fast depth extraction from a single image

2016 ◽  
Vol 13 (6) ◽  
pp. 172988141666337 ◽  
Author(s):  
Lei He ◽  
Qiulei Dong ◽  
Guanghui Wang
Keyword(s):  
Markov Random Fields ◽  
Image Data ◽  
Depth Estimation ◽  
Input Image ◽  
Estimation Accuracy ◽  
Range Image ◽  
Single Image ◽  
Scale Invariant ◽  
Sift Flow ◽  
Transfer Method

Predicting depth from a single image is an important problem for understanding the 3-D geometry of a scene. Recently, the nonparametric depth sampling (DepthTransfer) has shown great potential in solving this problem, and its two key components are a Scale Invariant Feature Transform (SIFT) flow–based depth warping between the input image and its retrieved similar images and a pixel-wise depth fusion from all warped depth maps. In addition to the inherent heavy computational load in the SIFT flow computation even under a coarse-to-fine scheme, the fusion reliability is also low due to the low discriminativeness of pixel-wise description nature. This article aims at solving these two problems. First, a novel sparse SIFT flow algorithm is proposed to reduce the complexity from subquadratic to sublinear. Then, a reweighting technique is introduced where the variance of the SIFT flow descriptor is computed at every pixel and used for reweighting the data term in the conditional Markov random fields. Our proposed depth transfer method is tested on the Make3D Range Image Data and NYU Depth Dataset V2. It is shown that, with comparable depth estimation accuracy, our method is 2–3 times faster than the DepthTransfer.

scholarly journals Robust Data Security Framework for IoT Network using Integrated Techniques

2020 ◽  
Vol 13 (1) ◽  
pp. 5-23
Author(s):  
Fadele Ayotunde Alaba ◽  
◽  
Abayomi Jegede ◽  
Christopher Ifeanyi Eke ◽  
◽  
...  
Keyword(s):  
Rapid Development ◽  
Cost Effective ◽  
Security Protocol ◽  
Security And Privacy ◽  
Security Framework ◽  
Resource Constrained ◽  
Iot Devices ◽  
Lightweight Authentication ◽  
Resource Constrained Devices ◽  
Constrained Devices

The Internet of Things (IoT) expects to improve human lives with the rapid development of resource-constrained devices and with the increased connectivity of physical embedded devices that make use of current Internet infrastructure to communicate. The major challenging in such an interconnected world of resource-constrained devices and sensors are security and privacy features. IoT is demand new approaches to security like a secure lightweight authentication technique, scalable approaches to continuous monitoring and threat mitigation, and new ways of detecting and blocking active threats. This paper presents the proposed security framework for IoT network. A detail understanding of the existing solutions leads to the development of security framework for IoT network. The framework was developed using cost effective design approach. Two components are used in developing the protocol. The components are Capability Design (mainly a ticket, token or key that provides authorization to access a device) and Advanced Encryption Standard (AES)-Galois Counter Mode (GCM) (a-security protocol for constrained IoT devices). AES-GCM is an encryption process that is based on authentication and well suitable IoT.

scholarly journals REMOTE SENSING SCENE CLASSIFICATION USING MULTIPLE PYRAMID POOLING

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 279-284
Author(s):  
Y. Yao ◽  
H. Zhao ◽  
D. Huang ◽  
Q. Tan
Keyword(s):  
Remote Sensing ◽  
Spatial Relationship ◽  
Image Data ◽  
Remote Sensing Image ◽  
Input Image ◽  
Image Size ◽  
Scene Classification ◽  
Fixed Size ◽  
Fixed Length ◽  
Fully Connected

<p><strong>Abstract.</strong> Remote sensing image scene classification has gained remarkable attention, due to its versatile use in different applications like geospatial object detection, ground object information extraction, environment monitoring and etc. The scene not only contains the information of the ground objects, but also includes the spatial relationship between the ground objects and the environment. With rapid growth of the amount of remote sensing image data, the need for automatic annotation methods for image scenes is more urgent. This paper proposes a new framework for high resolution remote sensing images scene classification based on convolutional neural network. To eliminate the requirement of fixed-size input image, multiple pyramid pooling strategy is equipped between convolutional layers and fully connected layers. Then, the fixed-size features generated by multiple pyramid pooling layer was extended to one-dimension fixed-length vector and fed into fully connected layers. Our method could generate a fixed-length representation regardless of image size, at the same time get higher classification accuracy. On UC-Merced and NWPU-RESISC45 datasets, our framework achieved satisfying accuracies, which is 93.24% and 88.62% respectively.</p>

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