Image encryption method based on multi-scale compressed sensing and Markov model

Patent metadata

Field	Value
Publication number	US-12118096-B2
Application number	US-202217948648-A
Country	US
Kind code	B2
Filing date	Sep 20, 2022
Priority date	Sep 18, 2021
Publication date	Oct 15, 2024
Grant date	Oct 15, 2024

Abstract

The present disclosure discloses an image encryption method based on multi-scale compressed sensing and a Markov model. According to the difference in information carried by low-frequency coefficients and high-frequency coefficients of an image, different sampling rates are set for the low-frequency coefficients and the high-frequency coefficients of the image, which can effectively improve the reconstruction quality of a decrypted image. The decrypted image obtained by the present disclosure has higher quality than the decrypted image generated by the existing scheme, and a better visual effect and more complete original image information can be obtained.

First claim

What is claimed is:

1. An image encryption method based on multi-scale compressed sensing and a Markov model, comprising:
generating parameters and initial values of one-dimensional chaotic mapping according to plaintext image information;
obtaining subsampling rates of original coefficient matrices at all levels by using multi-scale block compressed sensing theory through a total target sampling rate of a plaintext image;
substituting the parameters and initial values of the one-dimensional chaotic mapping into a corresponding chaotic system to generate chaotic sequences, transforming the chaotic sequences into a matrix form, and obtaining corresponding orthogonal basis matrices, and extracting some elements of the orthogonal basis matrices as measurement matrices according to the subsampling rates;
performing three-level discrete wavelet transform on the plaintext image to obtain the original coefficient matrices at all levels, and blocking the original coefficient matrices at all levels to construct new coefficient matrixes at all levels;
using the chaotic sequences to generate index sequences, and scrambling the corresponding coefficient matrices at all levels as blocks;
using the measurement matrices to measure the coefficient matrices at all levels;
retaining low-frequency coefficient matrices, merging measured non-low frequency coefficient matrices at all levels to construct a matrix T, performing micro-processing on the matrix T to obtain a to-be-measured matrix NT, and respectively generating a row-state transition probability matrix and a column-state transition probability matrix according to the to-be-measured matrix NT;
performing SVD decomposition on the low-frequency coefficient matrices to obtain submatrices, and quantizing the elements in the submatrices and the non-low frequency coefficient matrices at all levels to a preset interval;
generating index values according to the information of the chaotic sequences and determining merging rules by the index values, firstly mering the non-low frequency coefficient matrices at all levels according to the merging rules to obtain an overall matrix, and then inserting the submatrices into different positions of the overall matrix;
adjusting the dimension of the overall matrix, acquiring the element information of the overall matrix, and generating control parameters for secondary scrambling;
scrambling the merged overall matrix according to the row-state transition probability matrix and the column-state transition probability matrix, and simultaneously setting corresponding flag bits; and
performing independent diffusion of elements on the overall matrix by using one of the chaotic sequences, and performing global diffusion on elements of the overall matrix by using the other chaotic sequences to obtain a final encrypted image.

2. The image encryption method based on multi-scale compressed sensing and a Markov model of claim 1 , wherein a specific method of generating parameters and initial values of one-dimensional chaotic mapping according to plaintext image information is as follows:

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Assignees

• Univ Dalian Tech

Inventors

• Zhang Qiang
• Wang Bin
• Wang Pengfei
• Shi Yuandi
• Chen Rongrong
• Wei Xiaopeng

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F17/16 Primary

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• G06F21/602 Primary

  Providing cryptographic facilities or services · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title