Privacy-Preserving Random Permutation of Image Pixels Enciphered Model from Cyber Attacks for Covert Operations

Shakya, Amit Kumar; Ramola, Ayushman; Kandwal, Akhilesh; Chamoli, Vivek

doi:10.1007/978-981-13-1810-8_40

Cited by 4 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This technique of data representation was developed by "Karl Pearson" it looks similar to bar plots but is completely different from the bar plot. [105][106][107] Histogram analysis is quite useful in medical irregularity detection, 108,109 privacy maintenance, 110 data interpretation 111 resonance imaging, 112 image processing, 113 and so on.…”

Section: Histogrammentioning

confidence: 99%

Study of statistical methods for texture analysis and their modern evolutions

Ramola

Shakya

Pham

2020

Engineering Reports

Self Cite

100

View full text Add to dashboard Cite

Texture analysis is widely performed in the current time as it is considered as an intimate property of the surface. It is widely used in the field of image processing, remote sensing applications, biomedical analysis, document processing, and so on. In this investigation, we present a detailed study of four different methodologies that have been developed for texture classification. These methodologies include gray level cooccurrence matrix (GLCM), local binary pattern (LBP), autocorrelation function (ACF), and histogram pattern. The detailed investigation on these methods suggests that GLCM is best for analyzing the surface texture, land-use/landcover classification, and satellite data processing. LBP is widely used to analyze the facial features of an individual. The autocorrelation is used to identify the regularity of the textured surface. Finally, through histograms, one can visually identify the changes developed while analyzing the texture of the image data. Furthermore, we present a brief summary for newly developed texture classification techniques such as binary Gabor pattern, local spiking pattern, SRITCSD method, scale inversion, and deep perception models for texture analysis. Some benchmark texture datasets used in image processing are also discussed in this work. K E Y W O R D Sautocorrelation function, gray level cooccurrence matrix, histogram, local binary pattern, texture 1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Histogrammentioning

confidence: 99%

Study of statistical methods for texture analysis and their modern evolutions

Ramola

Shakya

Pham

2020

Engineering Reports

Self Cite

100

View full text Add to dashboard Cite

show abstract

“…10,11 This shortcoming gives birth to private content-based image retrieval (PCBIR) systems. [12][13][14] Many organizations in the corporate world have developed their PCBIR systems such as Viraz Incorporation, International Business Machine, Media Site Incorporation, Facebook, Google, and so on. 15,16 The main objective of PCBIR systems is to protect confidential data from cyber mugging and digital hacking by making queries through statistical features.…”

Section: F I G U R Ementioning

confidence: 99%

“…Assuming MPS, we have considered only 10% randomly selected image pixels from the total image pixels. An example of an image matrix representing the random pixel distribution is shown in the Equation (13).…”

Section: Mathematical Modelling Of the Proposed Algorithmmentioning

confidence: 99%

“…CBIR systems have a drawback of privacy‐related issues since all the queries are openly searched through search engines over the internet 10,11 . This shortcoming gives birth to private content‐based image retrieval (PCBIR) systems 12‐14 . Many organizations in the corporate world have developed their PCBIR systems such as Viraz Incorporation, International Business Machine, Media Site Incorporation, Facebook, Google, and so on 15,16 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Conversion of Landsat 8 multispectral data through modified private content‐based image retrieval technique for secure transmission and privacy

Shakya

Ramola

Vidyarthi

2020

Engineering Reports

Self Cite

View full text Add to dashboard Cite

In this research work, we have developed a new image encryption-based algorithm for Landsat 8 satellite images. Image statistical parameters related to first-order image statistics, that is, mean (μ), SD (σ), and variance (σ 2) are used to obtain the feature values for both original images and their encrypted versions. Multispectral satellite images generally contain a wide range of bands, which is 9 to 11 specifically for the Landsat 8 satellite. Hence, the information content of these images is richer than typical images. Through this algorithm, we aim to reduce the image content by randomly selecting only 10% of the total image pixels. These pixels are used to create new images, the so-called encrypted images. The visual presentation of the encrypted image resembles a noisy picture. First-order image statistics are calculated for both original images and their encrypted counterparts. These parameters show a similarity in their numerical values for both original and encrypted images. Thus, noisy pictures can be used for transmission in place of the original image when an outcome is decided through statistical image features. The visual similarity in the original images and encrypted images is obtained from the histogram signature plot for both original and encrypted images. The proposed algorithm can hence be used to convert image data for secure transmission and privacy.

show abstract