A Systematic Approach for Image Encryption Using Chaotic 2D Logistic Map Using MATLAB

A Systematic Approach for Image Encryption Using Chaotic 2D Logistic Map Using MATLAB

Harshitha S

Electronics and communication Rajarajeswari college of Engineering Bangalore , India

Ranjan D

Electronics and communication Rajarajeswari college of Engineering Bangalore, India

Madhushree B J

Electronics and communication Rajarajeswari college of Engineering Bangalore, India

Mrs. Chaithanya S

Ashwini L

Electronics and communication Rajarajeswari college of Engineering Bangalore, India

Assistant Professor, Department of ECE Rajarajeswari college of Engineering Bangalore, India

Abstract Chaos maps and chaotic systems have been proved to be useful and effective for cryptography. In this paper, the two- dimensional logistic map with complicated basin structures and attractors are first used for image encryption. The proposed method adopts the classic framework of the permutation- substitution network in cryptography and thus ensures both confusion and diffusion properties for a secure cipher. The proposed method is able to encrypt an intelligible image into random-like from the statistical point of view and the human visual system point of view. Extensive simulation results using test images from the USC SIPI image database demonstrate the effectiveness and robustness of the proposed method. Security analysis results of using both the conventional and the most recent tests show that the encryption quality of the proposed method reaches or excels the current state of the arts. Similar encryption ideas can be applied to digital data in other formats,

1. digital audio and video.

   Keywords Image Encryption, Two Dimensional Logistic Map, Key Schedule, Sequence Generator, 2D Logistic Permutation, 2D Logistic Diffusion, 2D Logistic Transposition Introduction.

   1. INTRODUCTION

      Image security attracts extensive concerns from the public and the government in recent years. Unexpected exposure of private photos and divulged military and governmental classified images emphasizes the importance of the image security again and again. With the fast development of digital storages, computers and the world wide network, a digital image can be easily copied to mobile storage or transferred to the other side of the world within a second. However, such convenience could also be used by malicious/unauthorized users to rapidly spread the image information that it may cause uncountable losses for the owner(s) of images.

      Among various image security technologies, the image encryption is a straight-forward one with concerns in encrypting an image to an unrecognized and unintelligent one, where the source image and the encrypted image are called plaintext image and ciphertext image, respectively. One common approach of image encryption is to treat the image data the same as the one-dimensional binary bit stream, which extracts a plaintext image bit by bit and then encrypts this binary bit stream. The advantage of this approach is able to encrypt a digital image using the existing block/stream ciphers designed originally for binary bit streams. These ciphers include the well known ciphers/standards: the Digital Encryption Standard (DES), the Advanced Encryption Standard (AES), the TwoFish cipher and the BlowFish cipher.

      In the research of image encryption algorithms/ciphers, efforts are found in two groups: optical image encryption, and digital image encryption. The former group adopts optics or optical instruments to build physical systems for image encryption, which commonly relies on optics to randomize frequency components in an image. The later 2 group commonly takes advantages of a digital image and encrypts it either by an encryption algorithm in the form of software or a physical electronic device in the form of hardware.

      Among various digital image encryption methods, the chaos- based image encryption method is a family of methods that are believed good for encryption purposes. Because a chaotic system has high sensitivities to its initial values, high sensitivities to its parameter(s), the mixing property and the ergodicity, it is considered as a good candidate for cryptography.

      In this paper, we adopt the two-dimensional Logistic map for image encryption in the first time with careful considerations for the diffusion and confusion properties and possible attacks as well. This chaotic map is researched with respect to its

      mathematical properties and physical dynamics previously and it has been showed that this coupled logistic map for two dimensions has more complicated chaotic behaviors like basin structures and attractors. We utilize this more complicated chaotic map to generate pseudo random sequences where we propose a key schedule algorithm to translate a binary encryption key to initial values and parameters used in the 2D logistic map. We develop an image encryption algorithm using these pseudo-random sequences under the framework of the permutation- substitution network, which is proven to be very effective to provide both confusion and diffusion properties in stream ciphers and block ciphers.

   2. THE TWO-DIMENSIONAL LOGISTIC MAP

      The two-dimensional logistic map is researched for its complicated behaviors of the evolution of basins and attractors. It has more complex chaotic behaviors than one- dimensional Logistic map.

      Mathematically, this 2D logistic map can be discretely defined as Equation (1), where r is the system parameter and (, ) is the pair-wise point at the iteration.

      Transposition. In every method the image will get ciphered and it provides more security for the image.

      Fig. 1: The flowchart of image encryption using the 2D logistic map.

      +1 = r(3 + 1) (1 )

      2D Logistic map: {

      +1 = r(3 + 1) (1 )

      (1)

      Fig. 2: The flowchart of image decryption using the 2D logistic map.

      B. 2D LOGISTIC PERMUTATION

      Without loss of generality, assume the size of the plaintext

   3. IMAGE ENCRYPTION USING THE 2D LOGISTIC MAP

      Although the 2D logistic map has various behaviors according to different system parameters, in the paper we concentrate on the parameter interval r [1.1, 1.19], where the system is chaotic.

      A. KEY SCHEDULE AND 2D LOGISTIC SEQUENCE GENERATOR

      image P is M×N. Therefore, the total number of pixels in P is MN. Consider the initial value used in a round is (0, 0) .A sequence of pair-wise x and y of length MN (exclude the initial value) can be generated via the 2D logistic map using Equation (1). Let be the x coordinate sequence and the y coordinate sequence of the 2D logistic map, respectively, as in Equation (3).

      Xseq = {x1, x2, . . . , xMN}

      { Y = {y , y , , y

      } (3)

      seq 1 2 MN

      We define our encryption key K as a 256-bit string composed of five parts 0, 0 , r, T, and 1. . . 8, where (0, 0) and r are the initial value and the parameter in the 2D logistic map defined in Equation (1), and A and T are the parameters of the linear congruential generator. Specifically speaking, we calculate a fraction value v from a 52-bit string using the IEEE 754 double-precision binary floating-point format for the fraction part as shown in Eq. (2)

      Rearrange elements of whose number is M×N in the matrix form and obtain M×N matrices X and Y, respectively. In the 2D logistic permutation we perform shuffling in row wise and column wise. In this stage we obtain cipher image which will be further encrypted in the following method.

      1. 2D LOGISTIC DIFFUSION

         In order to achieve good diffusion proprties, we apply the

         =

         52

         =1

         2

         (2)

         logistic diffusion for every S×S image block within the plaintext image P over the finite field Gf( ) as shown in

         Consequently, 0, 0, r and T, can be found. For coefficients1. . . 8, each of which is composed of 6-bit string

         {b0, b1 b5}, we translate these 6-bit strings to integers and

         obtain the required coefficients.

         Equation (4), where S is the block size variable determined by the plaintext image format, and Ld is the maximum distance separation matrix found from 4×4 random permutation matrices defined in Equation (5).

         = (.. . )28

         = (1. . 1)28

         (4)

   4. METHODOLOGY

      4 2 1 3

      71 216 173 117

      Here, in this paper we mainly proceed with three methods: 2D

      = [1 3 4 2] and ( 1)28 = [ 173 117 71 216 ] (5)

      Logistic Permutation, 2D Logistic Diffusion, 2D Logistic

      2 4 3 1

      3 1 2 4

      216 71 117 173

      117 173 216 71

      1. 2D LOGISTIC TRANSPOSITION

      Unlike diffusion stages used in conventional diffusion- permutation network, the 2Dlogistic transposition process changes pixels values with respect to the reference image I, which is dependent on the logistic sequence generated from the previous stage.

      First X and Y which the matrix version of by arranging a sequence elements in a matrix, are added together to be Z via Equation (6).

      Z = X+Y (6)

      Furthermore, each 4×4 block B in Z is then translated to a (pseudo) random integer matrix using the block function f(B) as shown in Equation (7), where B is a 4 × 4 block, and the sub function (. ), (. ), (. ), (. ) are defined in Equation (8)-(11). The function T (d) truncates a decimal d from the 9th digit to 16th digit to form an integer, for example if b = 0.12345678901234567890, then T (d) = 90123456. The

      symbol F denotes the number of allowed intensity scales of the plaintext image format. In other words, F = 2 if the plaintext image P is a binary image and F = 256 if P is an 8-bit gray image.

   5. RESULT

      Thus the image will be more secured by undergoing encryption for so many times. The decryption process is obtained in the reverse process of the encryption.

      Fig 3: This figure shows the Plain image and encrypted image with their histogram respectively

      (1,1) (1,2) (1,3) (1,4)

      = () = (2,1) (2,2) (2,3) (2,4)

      (3,1) (3,2) (3,3) (3,4)

      (7)

      [ (4,1) (4,2) (4,3) (4,4)]

      () = () (8)

      () = [() ] (9)

      () = (2) (10)

      () = (2) (11)

      When function f(.) is applied to all 4×4 block within the 2D logistic map associated random like matrix Z without overlapping, then a random integer matrix I is obtained, where each 4 × 4 block in I is actually mapped from a corresponding 4 × 4 block in Z with the function f(.) defined in Equation (7).

      Finally, the 2D logistic transposition is achieved by shifting the each pixel in the plaintext image with the specified amount of the random integer image I over the integer space [0 , F1] ,

      i.e. the ciphertext image of 2D logistic map C is defined as Equation (12), where F is the number of allowed intensity scales of the plaintext image. For example, F = 256 for a 8-bit grayscale image.

      C = (P+I) mod F (12)

      Similarly, we can use Equation (13) for decryption.

      P = (C-I) mod F (13)

      .

      Fig 4: This figure shows the Decrypted image with its histogram.

   6. CONCLUSION

In this paper, the two-dimensional logistic map is used for image encryption for the first time. Unlike the conventional one-dimensional logistic map, the two-dimensional logistic map has chaotic behaviors in an additional dimension includes a time asymmetric feedback for two dimensions with both basins and attractors in evolution. Consequently, the pseudo number sequences generated from the two-dimensional logistic map for image encryption are more random-like and complicated.

The proposed image encryption method adopts a permutation- substitution network structure with good confusion and diffusion properties, where each cipher round includes the three encryption stages: 2D Logistic Permutation, 2D Logistic Diffusion and 2D Logistic Transposition, each of which is an image cipher. In such a way, the proposed image encryption method is able to resist many existing cryptography attack.

Extensive experimental results show that the proposed image encryption method is able to encrypt intelligible plaintext images to random-like ciphertext images. In other words, a ciphertext image obtained from the proposed image cipher is

unrecognizable and unintelligible and its statistical properties are very similar to those of a random image.

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