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Image encryption method based on cellular automata and partition compression perception

A block compressive sensing, cellular automaton technology, applied in the field of information security, can solve the problems of unfavorable data transmission, large data volume, and high information redundancy

Active Publication Date: 2017-12-15
HENAN UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Because the amount of image data is generally relatively large, the information redundancy is high, which is not conducive to data transmission.

Method used

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  • Image encryption method based on cellular automata and partition compression perception
  • Image encryption method based on cellular automata and partition compression perception
  • Image encryption method based on cellular automata and partition compression perception

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Experimental program
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Effect test

Embodiment 1

[0072] Embodiment 1. Due to the large amount of image data, high information redundancy, and unfavorable data transmission, etc., in information security image encryption, in order to reduce the amount of network transmission data, the image needs to be compressed for transmission, and the image compression and Encrypted efficient fusion, see figure 1 As shown, an image encryption method based on cellular automata and block compressed sensing is provided, which includes the following content: the plaintext image obtains the initial state value and parameters of the variable parameter chaotic system through the SHA256 function; the plaintext image is decomposed by single-layer wavelet Decompose into three high-frequency component matrices and one low-frequency component matrix, and use elementary cellular automata to scramble the four component matrices; bring the initial state values ​​and parameters of the variable parameter chaotic system into the variable parameter chaotic s...

Embodiment 2

[0073] Embodiment two, see figure 2 As shown, combining the compressed sensing and the cellular automata, using the elementary cellular automata to scramble the image, and compressing and encrypting the image through the compressed sensing, the specific implementation steps are as follows:

[0074] Step 1: Use the SHA256 function to calculate the plaintext image P with a size of M×N, obtain a set of 256-bit hash values ​​and use it as the image key Key, and convert the 256-bit image key Key into 32 decimals number k 1 , k 2 ,...,k 32 , to calculate the initial state value and parameters of the variable parameter chaotic system. The variable parameter chaotic system is composed of a one-dimensional piecewise linear iterative chaotic map (PLCM) and a Logistic chaotic map with good random statistical properties. PLCM is used to generate chaotic sequences. Logistic chaotic map to generate the parameters of PLCM, the expression of PLCM is:

[0075]

[0076] , whose output s...

Embodiment 3

[0125] Embodiment three, see Figure 3-7 As shown, in this embodiment, the programming software used is Matlab R2016b, and the Lena grayscale image with a size of 512×512 is selected as the experimental object. The specific encryption process is as follows:

[0126] Step 1: Input the Lena grayscale image with the original size of 512×512, use P=imread('Lena.bmp') to read the image information, use the SHA256 function to calculate the plaintext image P, and obtain a set of 256-bit hashes Value and use it as the image key Key, and then convert the 256-bit Key to 32 decimal numbers k 1 , k 2 ,...,k 32 , the specific steps used to calculate the initial state value of the variable parameter chaotic system are as follows:

[0127] 1.1) A variable parameter chaotic system is adopted, which is composed of a one-dimensional piecewise linear iterative chaotic map (PLCM) and a Logistic chaotic map with good random statistical properties. The PLCM is used to generate a chaotic sequence...

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Abstract

The invention relates to an image encryption method based on a cellular automata and partition compression perception; the method comprises the following steps: using a single layer wavelet decomposition mode to decompose an image into four matrixes containing high frequency and low frequency, and using primary cellular automata to respectively scramble the matrixes; using different compression ratios to carry out compression perception, using a variable element chaotic system to build a measuring matrix, wherein the chaotic system initial value and parameters are formed by a plaintext image SHA256 function; regrouping the compressed four matrixes, scrambling the regrouped matrixes, thus obtaining a cryptograph image. The method uses the primary cellular automata to scramble the image so as to improve the algorithmic security, uses the compression perception to compress and encrypt the image, thus reducing the data bulk; a secret key space is added on one hand, thus increasing the algorithm anti-violence attack capability; on the other hand, the secret key generation is tightly depended on the plaintext, thus improving the algorithm anti-selection plaintext attack capability.

Description

technical field [0001] The invention belongs to the technical field of information security and relates to an image encryption method, in particular to an image encryption method based on cellular automata and block compression sensing. Background technique [0002] Today, with the rapid development of computer technology, multimedia technology and digital signal processing technology, more and more images, especially digital images, are transmitted and saved through the network. While network technology brings convenience to people, some criminals also use the network to steal information, such as personal privacy, commercial secrets, and even military secrets. These behaviors may damage the interests of individuals and companies, and even threaten national security. Therefore, information security has been paid more and more attention by people and countries. Generally speaking, image information has the same characteristics, and they all have the characteristics of large...

Claims

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Application Information

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IPC IPC(8): G06T1/00G06T9/00
Inventor 柴秀丽符翔龙魏丹甘志华路杨张继通
Owner HENAN UNIVERSITY
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