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Image encryption method based on memristive chaotic system, elementary cellular automaton (ECA) and compression perception

A cellular automata and chaotic system technology, applied in image data processing, image data processing, instruments, etc., can solve problems such as low correlation of algorithm keys, image information theft, and difficulty in resisting chosen-plaintext attacks.

Active Publication Date: 2017-12-19
HENAN UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, there are also potential security risks in the network, especially in the process of image transmission, which faces many problems: image information is stolen and tampered by criminals, affected by noise during transmission, etc. These factors make the security of modern multimedia data a top priority.
However, the proposed algorithm has low correlation between the key and the plaintext, and it is difficult to resist the chosen plaintext attack. In addition, the redundant data of the encrypted image has not been reduced.

Method used

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  • Image encryption method based on memristive chaotic system, elementary cellular automaton (ECA) and compression perception
  • Image encryption method based on memristive chaotic system, elementary cellular automaton (ECA) and compression perception
  • Image encryption method based on memristive chaotic system, elementary cellular automaton (ECA) and compression perception

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Experimental program
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Embodiment 1

[0083] Embodiment 1, for the existing image encryption process is not highly correlated with the plaintext or the key space is not large enough, see figure 1 As shown, this embodiment provides an image encryption method based on the memristive chaotic system, elementary cellular automata and compressed sensing. The plaintext image uses the SHA-512 function to obtain the initial value of the memristive chaotic system and the initial structure of the cellular automata. type; the plaintext image is transformed by discrete wavelets to obtain a sparse coefficient matrix; the sparse coefficient matrix is ​​scrambled by the zigzag scrambling method, and then the elementary cellular automata is used for the scrambling operation; the initial value of the memristive chaotic system is brought into the memory The chaotic system generates the measurement matrix, and compresses the scrambled matrix through the measurement matrix to obtain the final ciphertext image.

[0084] Using the image...

Embodiment 2

[0085] Embodiment two, see figure 2 As shown, an image encryption method based on memristive chaotic system, elementary cellular automata and compressed sensing is provided, and its implementation process specifically includes the following steps:

[0086] Step 1. Use the discrete wavelet transform DWT to transform the plaintext image I with a size of N×N to obtain a sparse coefficient matrix I with a size of N×N 1 .

[0087] Step 2. Use the SHA-512 function to calculate the plaintext image I, obtain a set of 512-bit hash values ​​and use it as the image key Key, and then convert the 512-bit image key Key into 64 decimal numbers k 1 ,k 2 ,...,k 64 , to calculate the initial value x of the memristive chaotic system 0 、y 0 , z 0 、w 0 ; Specifically calculate the initial value of the memristive chaotic system through the following steps:

[0088] Step 2.1, convert the 512-bit key Key into a group of 8 bits into 64 decimal numbers k 1 ,k 2 ,...,k 64 , and then use the ...

Embodiment 3

[0155] Embodiment three, see Figure 8-11 As shown, in this embodiment, the programming software used is Matlab R2016a, and the Lena grayscale image with a size of 512×512 is selected as the experimental object, and the specific encryption process is as follows:

[0156] Step 1: Input the Lena grayscale image with the original size of 512×512, use I=imread('Lena.bmp') to read the image information, and use the discrete wavelet transform (DWT) to transform the image I to obtain a size of 512 ×512 sparse coefficient matrix I 1 .

[0157] Step 2: Use the SHA-512 function to calculate the plaintext image I, get a set of 512-bit hash values ​​and use it as the image key Key, and then convert the 512-bit image key Key into 64 decimal numbers k 1 ,k 2 ,...,k 64 , to calculate the initial value of the memristive chaotic system. Specific steps are as follows:

[0158] 2.1) Use the SHA-512 function to calculate the plaintext image I to obtain a set of 512-bit hash values ​​(expres...

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Abstract

The invention belongs to the field of image encryption, and particularly relates to an image encryption method based on a memristive chaotic system, an elementary cellular automaton (ECA) and compression perception. The method includes: firstly, carrying out discrete wavelet transform on an image, and then obtaining a sparse coefficient matrix; then adopting a zigzag scrambling method to carry out scrambling on the sparse coefficient matrix, and then utilizing the elementary cellular automaton for scrambling operation; and finally, using a measurement matrix, which is generated by the memristive chaotic system, to carry out compression perception on the image after scrambling to obtain a final ciphertext image. The plaintext image generates initial values of the chaotic system and an initial configuration of the cellular automaton through being applied to SHA-512 functions, and correlation between an algorithm and the plaintext image is enhanced. According to the method, image encryption technology of combining the elementary cellular automaton and compression perception is adopted , the elementary cellular automaton is utilized to carry out scrambling on the image, the image is encrypted while image compression is realized through compression perception, the amount of transmitted data is reduced, and image information leakage is prevented. The method has higher safety performance.

Description

technical field [0001] The invention relates to the field of image encryption, in particular to an image encryption method based on a memristive chaotic system, elementary cellular automata and compressed sensing. Background technique [0002] In recent years, with the advent of the Internet age, most of the information in our lives is inseparable from the support of the network. We use the network to conduct video conferences and send some confidential information. Images are widely used as the carrier of information, and digital images are more and more frequently transmitted and processed on the network due to their advantages of long-term storage and easy transmission. However, there are also potential security risks in the network, especially in the process of image transmission, which faces many problems: image information is stolen and tampered by criminals, and is affected by noise during transmission. These factors make the security of modern multimedia data an urge...

Claims

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Application Information

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IPC IPC(8): G06T1/00
Inventor 柴秀丽郑晓宇郑泰皓甘志华路杨武海洋
Owner HENAN UNIVERSITY
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