SVD‐based image compression, encryption, and identity authentication algorithm on cloud

Yu, Changzhi; Li, Hengjian; Wang, Xiyu

doi:10.1049/iet-ipr.2018.5912

Cited by 19 publications

(8 citation statements)

References 28 publications

(30 reference statements)

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“…In this regard, singular value decomposition is preferred over other feature extraction algorithms because it proved its efficiency in dealing with a wide range of engineering applications including forecasting weekly solar radiation ( 59 ), streamflow forecasting ( 60 ), and acoustic event classification ( 61 ). Additionally, it is characterized by its low computational complexity ( 62 , 63 ). It is also worth mentioning that singular value decomposition demonstrated superior dimensionality reduction accuracy against principal component analysis according to a set of performance evaluation tests ( 64 , 65 ).…”

Section: Proposed Methodsmentioning

confidence: 99%

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Abdelkader

Moselhi

Marzouk

et al. 2020

Transportation Research Record

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Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

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Section: Proposed Methodsmentioning

confidence: 99%

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Abdelkader

Moselhi

Marzouk

et al. 2020

Transportation Research Record

View full text Add to dashboard Cite

show abstract

“…SVD is mostly applicable in image-based feature extraction. For example, Yu et al [ 77 ] proposed an SVD-based authentication scheme. They used SVD to decompose image data into three matrices of left singular value, right singular value, and singular value and performed authentication through a value calculation method based on the singular value matrix.…”

Section: Machine Learning Models In Authentication Schemes Of Telehealthmentioning

confidence: 99%

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Fard

Gebali

Mamun

2022

Sensors

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Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applications to propose robust authentication protocols. Since machine learning methods are trained based on hidden concepts in biometric and physical layer data, these dynamic authentication models can be more reliable than traditional methods. The main advantage of these methods is that the behavioral traits of humans and devices are tough to counterfeit. Furthermore, machine learning facilitates continuous and context-aware authentication.

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“…3) SVD: is a mathematical method to decompose a single matrix by compressing it into three smaller matrices of the same size by reducing the data in columns and rows [13]. Each matrix M in the SVD, which is n × n in size, can be broken down into three parts as in (1) [19]:…”

Section: ) Coiflets: Discrete Wavelet Designed By Ingridmentioning

confidence: 99%

Compression Analysis Using Coiflets, Haar Wavelet, and SVD Methods

Abdillah¹,

Yudhana²,

Fadlil³

2021

Jurnal Informatika

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The image problem lies in the amount of storage space required, to save memory as little as possible image compression is required. The image compression technique is a technique used to represent an image by reducing the quality of the original image but still retaining the information inside. This study compares the best compression method between Coiflets, Haar wavelets, and SVD with JPG image material. The comparison process has done by calculating the compression ratio (CR), Space Saving (SS), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Peak Signal to Noise Ratio (PSNR). The results obtained prove that the SVD method has the highest compression ratio of 3.25 while in the case of Space Saving (SS) the Coiflets method gives the best performance with a value of 73. Measurement in terms of MSE and RMSE is the best for the Coiflets method because it has an average value. -The smallest average among all methods is 0.02395 and 0.111383. provides the best performance in maintaining compression quality. The best PSNR based image quality assessment is the Coiflets method with the highest PSNR average of 63.02 dB. Overall, the Coiflets, Haar wavelet, and SVD compression methods used for JPG images can reduce file size and preserve image information and quality.

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SVD‐based image compression, encryption, and identity authentication algorithm on cloud

Cited by 19 publications

References 28 publications

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Compression Analysis Using Coiflets, Haar Wavelet, and SVD Methods

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