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A highly nonlinear S-box based on a fractional linear transformation

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ABSTRACT

We study the structure of an S-box based on a fractional linear transformation applied on the Galois field \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$GF(2^{8})$$\end{document}GF(28). The algorithm followed is very simple and yields an S-box with a very high ability to create confusion in the data. The cryptographic strength of the new S-box is critically analyzed by studying the properties of S-box such as nonlinearity, strict avalanche, bit independence, linear approximation probability and differential approximation probability. We also apply majority logic criterion to determine the effectiveness of our proposed S-box in image encryption applications.

No MeSH data available.


Histogram of the images in Fig. 6. a Plain image. b Encrypted Image
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Fig7: Histogram of the images in Fig. 6. a Plain image. b Encrypted Image

Mentions: For an image, its histogram graphically represents image-pixels distribution by plotting the number of pixels at each intensity level (Ramirez-Torres et al. 2014). It has been established that the histogram of the original and the encrypted image should be significantly different so that attackers could not extract the original image from the encrypted one. Figure 7 shows the respective histograms of Lena’s plain image and its encrypted version. The histogram analysis evidently proves the stability of our proposed method against any histogram based attacks.


A highly nonlinear S-box based on a fractional linear transformation
Histogram of the images in Fig. 6. a Plain image. b Encrypted Image
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037109&req=5

Fig7: Histogram of the images in Fig. 6. a Plain image. b Encrypted Image
Mentions: For an image, its histogram graphically represents image-pixels distribution by plotting the number of pixels at each intensity level (Ramirez-Torres et al. 2014). It has been established that the histogram of the original and the encrypted image should be significantly different so that attackers could not extract the original image from the encrypted one. Figure 7 shows the respective histograms of Lena’s plain image and its encrypted version. The histogram analysis evidently proves the stability of our proposed method against any histogram based attacks.

View Article: PubMed Central - PubMed

ABSTRACT

We study the structure of an S-box based on a fractional linear transformation applied on the Galois field \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$GF(2^{8})$$\end{document}GF(28). The algorithm followed is very simple and yields an S-box with a very high ability to create confusion in the data. The cryptographic strength of the new S-box is critically analyzed by studying the properties of S-box such as nonlinearity, strict avalanche, bit independence, linear approximation probability and differential approximation probability. We also apply majority logic criterion to determine the effectiveness of our proposed S-box in image encryption applications.

No MeSH data available.