Image scrambling method based on dynamic random rotation model

Abstract

In many fields, a large number of images are generated every day. In order to protect image information from being stolen and improve security and efficiency in a network transmission process, the invention provides an image scrambling method based on a dynamic random rotation model. The method comprises the following steps: firstly, dynamically selecting a random rotation center by using two chaos sequences; secondly, selecting a dynamic random rotation angle by using a chaos sequence; thirdly, calculating the rotation radius according to the rotation center, and determining the position and size of the image block needing to be dynamically and randomly rotated; and finally, dynamically and randomly rotating the image according to the rotation center, the rotation radius and the rotation angle to obtain a scrambled image. According to the method, a dynamic random rotation model is utilized to realize image scrambling operation on an original image. Experimental results show that the method achieves good results in the aspects of visual effect, scrambling degree, efficiency and the like, and can protect the security of image transmission and storage under a network platform.

Description

Technical Field

[0001] The present invention relates to an information encryption technology, in particular to an image encryption method. Background Art

[0002] In recent years, people have paid more and more attention to the security issues of networks and information systems, and images have become an important information carrier in people's daily lives. It can convey a large amount of information intuitively and vividly, and has been widely used in communications, military and medical fields. However, due to the openness of the Internet, information is easily intercepted or leaked when it is transmitted in the network. Data hiding and image encryption are common methods to keep images secure, but the former is subject to some limitations due to insufficient embedding capabilities. In contrast, image encryption can protect images more effectively. Therefore, how to encrypt images effectively and securely becomes very important.

[0003] Because images have large data capacity and high correlation between pixels and data redundancy, traditional data encryption algorithms are usually not suitable for encrypting digital images. In recent years, various image encryption algorithms based on chaos have been proposed, such as chaos-based image encryption, compressed sensing-based image encryption, and genetic algorithm-based image encryption. With the advent of big data, the network information transmission capacity has also been greatly improved, and the efficiency and security requirements of encryption algorithms have been further improved.

[0004] In order to protect image information from being stolen and improve the security and efficiency of the algorithm during network transmission, an image scrambling method based on a dynamic random rotation model is proposed. This method achieves effective scrambling of the image through dynamic random rotation, improving the security and efficiency of the algorithm. Summary of the invention

[0005] The purpose of the present invention is to protect image information from being stolen and to improve the security and efficiency of the algorithm during network transmission. An image scrambling method based on a dynamic random rotation model is proposed.

[0006] Technical solution of the present invention: To achieve the above-mentioned purpose of the invention, the technical solution adopted is an image scrambling method based on a dynamic random rotation model, and the scrambling steps are described in detail as follows:

[0007] Step 1: Generate chaotic sequence: Logistic mapping is:

[0008] x t+1 =rx t (1+x t ), (1)

[0009] Among them, r∈[3.57,4), x t∈(0,1); let h be the number of image rotations, randomly select r and the initial value x0, and iterate formula (1) for 3h times to obtain three chaotic sequences K1, K2 and K3 of length h;

[0010] Step 2: Integerization of chaotic sequence: Let the original image be I 0 , the size is m×n; use formulas (2)-(4) to convert K1, K2 and K3 into integers respectively,

[0011] K4=mod(K1×10 15 , m)+1, (2)

[0012] K5=mod(K2×10 15 , n)+1, (3)

[0013] K6=mod(K3×10 15 , 7), (4)

[0014] Where mod(·) is the modulus function;

[0015] Step 3: Dynamically select random rotation centers: Use K4 and K5 to dynamically select h random rotation centers: (K4(i), K5(i)), K4(i)∈K4, K5(i)∈K5, i=1,2,...,h;

[0016] Step 4: Dynamically select a random rotation angle: Let K6(i)∈K6, and use K6 to determine the rotation angle of each image block; when K6(i)=0, the image block is rotated 0° clockwise; when K6(i)=1, the image block is rotated 90° clockwise; when K6(i)=2, the image block is rotated 180° clockwise; when K6(i)=3, the image block is rotated 270° clockwise; when K6(i)=4, the image block is rotated 90° counterclockwise; when K6(i)=5, the image block is rotated 180° counterclockwise; when K6(i)=6, the image block is rotated 270° counterclockwise;

[0017] Step 5: Calculate the rotation radius: Calculate the i-th rotation center (K4(i), K5(i)) to the i-1th rotation image I i-1 The distances of the top, left, bottom, and right borders are r respectively. 1i =K4(i), r 2i =K5(i), r 3i =m-K4(i)+1 and r 4i =n-K5(i)+1; Calculate the rotation radius r inside the image using formula (5) i for:

[0018] r i =min(r1i , r 2i , r 3i , r 4i ), (5)

[0019] Where i = 1, 2, ..., h, min(·) is the function of taking the minimum value;

[0020] Step 6: Image Dynamic Random Rotation Model: In I i-1 In the figure, (K4(i), K5(i)) is the rotation center, and r i is the radius of rotation, and the horizontal coordinate value is from K4(i)-r i +1 to K4(i)+r i -1 and a ordinate K5(i)-r i +1 to K5(i)+r i -1 is a square image block composed of I i-1 Perform the i-th random rotation, the direction and degree of random rotation are determined by K6(i), and the rotated image I is obtained. i , where i = 1, 2, ..., h; the h-th rotation image I h , which is the final scrambled image.

[0021] The image decryption process is the inverse process of image scrambling.

[0022] Beneficial effects: In order to protect image information from being stolen and to improve the security and efficiency of the algorithm during network transmission, an image scrambling method based on a dynamic random rotation model is proposed. The main contributions are as follows: (1) Inspired by the disturbance of the horizontal plane by water ripples, the concepts of image rotation center and rotation radius are given, and an image dynamic random rotation model is designed; (2) Using the dynamic random rotation model, an image scrambling method based on the dynamic random rotation model is proposed to realize the scrambling operation of the original image; (3) Experimental results show that the proposed image scrambling method achieves good results in terms of visual effect, scrambling degree and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 : Scrambling flow chart;

[0024] Figure 2 : original image;

[0025] Figure 3 : Scramble the image;

[0026] Figure 4 : Dynamically randomly rotate the scrambled image 10 times. DETAILED DESCRIPTION

[0027] The implementation process of the present invention is further described in detail below with reference to specific drawings and examples.

[0028] Figure 1 This is the scrambling flow chart of this method.

[0029] The programming software used is Matlab R2022a, and the public database USC-SIPI is selected. Figure 2 The grayscale image of Bridge with a size of 512×512 is used as the experimental object (image URL: Http: / / sipi.usc.edu / database). The experimental process is dynamic random rotation 10,000 times.

[0030] Step 1: Generate chaotic sequences: Iterate formula (1) 3×10000 times and divide the generated chaos into three equal parts to obtain three chaotic sequences K1, K2 and K3 with a length of 10000.

[0031] Step 2: Integerization of chaotic sequence: Let the original image be I 0 , the size is 512×512; use formulas (2)-(4) to convert K1, K2 and K3 into integers respectively.

[0032] Step 3: Dynamically select 10,000 random rotation centers: Use K4 and K5 to dynamically select 10,000 random rotation centers as (K4(i), K5(i)), K4(i)∈K4, K5(i)∈K5, i=1,2,...,10,000.

[0033] Step 4: Dynamically select 10,000 random rotation angles: K6(i)∈K6, i=1,2,...,10,000, and use K6(i) to determine the rotation angle within the image each time. When K6(i)=0, the image block is rotated 0° clockwise; when K6(i)=1, the image block is rotated 90° clockwise; when K6(i)=2, the image block is rotated 180° clockwise; when K6(i)=3, the image block is rotated 270° clockwise; when K6(i)=4, the image block is rotated 90° counterclockwise; when K6(i)=5, the image block is rotated 180° counterclockwise; when K6(i)=6, the image block is rotated 270° counterclockwise.

[0034] Step 5: Calculate 10,000 rotation radii: Calculate the i-th rotation center (K4(i), K5(i)), i = 1, 2, ..., 10,000, to the i-1th rotation image I i-1 The distances of the top, left, bottom, and right borders are: r 1i =K4(i), r 2i =K5(i), r 3i=m-K4(i)+1 and r 4i =n-K5(i)+1; Calculate the rotation radius r of the image block using formula (5) i , you can get 10,000 random rotation radii.

[0035] Step 6: Dynamic random rotation of the image: take (K4(i), K5(i)) as the rotation center and r i is the radius of rotation, and the horizontal coordinate value is from K4(i)-r i +1 to K4(i)+r i -1 and a ordinate K5(i)-r i +1 to K5(i)+r i -1 is a square image block composed of random rotation direction and degree determined by K6(i) to achieve I i-1 The i-th random rotation of i , where i=1,2,...,10000,I 10000 That is the final scrambled image, such as Figure 3 As shown. During the rotation process, the scrambled image I obtained by the 10th dynamic random rotation 10 ,like Figure 4 shown.

[0036] The image decryption process is the inverse process of image scrambling.

Claims

1. An image scrambling method based on a dynamic random rotation model, characterized in that: The scrambling process includes the following steps: Step 1: Generate chaotic sequence: Logistic mapping is: x t+1 =rx t (1+x t ), (1) Among them, r∈[3.57,4), x t ∈(0,1); let h be the number of image rotations, randomly select r and the initial value x0, iterate formula (1) 3h times, and divide the generated chaos into three equal parts, and we can get three chaotic sequences K1, K2 and K3 with a length of h; Step 2: Integerization of chaotic sequence: Let the original image be I 0 , the size is m×n; use formulas (2)-(4) to convert K1, K2 and K3 into integers respectively, K4=mod(K1×10 15 ,m)+1, (2) K5=mod(K2×10 15 ,n)+1, (3) K6=mod(K3×10 15 ,7), (4) Where mod(·) is the modulus function; Step 3: Dynamically select random rotation centers: Use K4 and K5 to dynamically select h random rotation centers: (K4(i), K5(i)), K4(i)∈K4, K5(i)∈K5, i=1,2,...,h; Step 4: Dynamically select a random rotation angle: Let K6(i)∈K6, and use K6 to determine the rotation angle of each image block; when K6(i)=0, the image block is rotated 0° clockwise; when K6(i)=1, the image block is rotated 90° clockwise; when K6(i)=2, the image block is rotated 180° clockwise; when K6(i)=3, the image block is rotated 270° clockwise; when K6(i)=4, the image block is rotated 90° counterclockwise; when K6(i)=5, the image block is rotated 180° counterclockwise; when K6(i)=6, the image block is rotated 270° counterclockwise; Step 5: Calculate the rotation radius: Calculate the i-th rotation center (K4(i), K5(i)) to the i-1th rotation image I i-1 The distances of the top, left, bottom, and right borders are: r 1i =K4(i), r 2i =K5(i), r 3i =m-K4(i)+1 and r 4i =n-K5(i)+1; Using formula (5), calculate the rotation radius r of the image block i for: r i =min(r 1i ,r 2i ,r 3i ,r 4i ), (5) Where i = 1, 2, ..., h, min(·) is the function of taking the minimum value; Step 6: Image Dynamic Random Rotation Model: In I i-1 In the example, (K4(i), K5(i)) is the rotation center and r i is the radius of rotation, and the horizontal coordinate value is from K4(i)-r i +1 to K4(i)+r i -1 and the ordinate K5(i)-r i +1 to K5(i)+r i -1 is randomly rotated for the i-th time. The specific random rotation direction and degree are determined by K6(i). The rotated image I i , i=1,2,...,h; the h-th rotation image I h , which is the final scrambled image.

Images