2020
DOI: 10.3390/e22010073
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An Image Encryption Algorithm Based on Random Hamiltonian Path

Abstract: In graph theory, Hamiltonian path refers to the path that visits each vertex exactly once. In this paper, we designed a method to generate random Hamiltonian path within digital images, which is equivalent to permutation in image encryption. By these means, building a Hamiltonian path across bit planes can shuffle the distribution of the pixel’s bits. Furthermore, a similar thought can be applied for the substitution of pixel’s grey levels. To ensure the randomness of the generated Hamiltonian path, an adjuste… Show more

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Cited by 15 publications
(2 citation statements)
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“…In the second approach for the secret data embedding, they used the four identical cover images, and then the secret data of cover images were embedded in two phases. In the first phase, the N-RMB (NRightmost Bit replacement) techniques were used, and in the second phase, the MPVD (Modified Pixel Value Differencing) technique they used in [52], 1D adaptive Bernoulli map, ideal for encryption, is suggested for correlation coefficients. The revolutionary image encryption technique was implemented focused on the recent chaotic map.…”
Section: Preliminariesmentioning
confidence: 99%
“…In the second approach for the secret data embedding, they used the four identical cover images, and then the secret data of cover images were embedded in two phases. In the first phase, the N-RMB (NRightmost Bit replacement) techniques were used, and in the second phase, the MPVD (Modified Pixel Value Differencing) technique they used in [52], 1D adaptive Bernoulli map, ideal for encryption, is suggested for correlation coefficients. The revolutionary image encryption technique was implemented focused on the recent chaotic map.…”
Section: Preliminariesmentioning
confidence: 99%
“…Information entropy is an essential feature of the randomness of an input image. Entropy is the average (expected) amount of information from the data [30], [31]. For a digital image, it is hard to predict the content if its information entropy is high.…”
Section: Information Entropymentioning
confidence: 99%