Bit-Level Encryption and Decryption of Images Using Genetic ... - IPASJ

IPASJ International Journal of Information Technology (IIJIT) Web Site: http://www.ipasj.org/IIJIT/IIJIT.htm Email: [email protected] ISSN 2321-5976

A Publisher for Research Motivation ........

Volume 1, Issue 6, December 2013

Bit-Level Encryption and Decryption of Images Using Genetic Algorithm: A New Approach Gamil R. S. Qaid1, Sanjay N. Talbar2 1

Research Student, Electronics & Telecommunications Dept.,S.G.G.S. institute of Engineering and Technology, Nanded, India. 2 Professor Dept., Electronics & Telecommunications,S.G.G.S. institute of Engineering and Technology, Nanded, India.

Abstract Encryption is one of the popular methods to achieve secret communication between sender and receiver. In current time the security of digital images draws more attention, especially when these digital images are stored in memory or send through the communication networks. Many different image encryption techniques have been proposed to save the security of images. Image encryption techniques try to convert an image to another image that is hard to understand. Genetic Algorithm (GA) has been popular in the encryption image because of its intuitiveness and ease of implementation. This paper proposes a method based on Genetic Algorithm (GA) which is used to produce a new encryption method by exploiting the powerful features of the Crossover and Mutation operations of (GA). It is a new approach of genetic algorithm (GA) with pseudorandom sequence to encrypt image stream. The feature of this approach includes high security and high feasibility for easy integration with digital image transmission applications. The experimental results of the proposed technique confirmed that high throughput rate required for real time data protection was achieved.

Key words: Encryption, Decryption, Genetic algorithm, Image, Crossover, mutation, Cryptography.

1. INTRODUCTION With the increasing use of the Internet and other efficient communication technologies, the digital media have become the most common tools used to exchange data. Most of these digital media are in an image form and used in various applications, such as websites, email, chats, e-commerce, eBooks, news, etc. However, digital content is still confronted with several challenges, such as copyright protection, tampering, and authentication. The modern encryption techniques have been considered the most powerful solution to most of these problems. Content authentication and tamper detection of digital image, video and audio have captured interest of researchers. Recently, content authentication, protection, copyright and tamper detection of images attracted the interest of researchers. During the last ten years, researches on image security schemes focused mainly on issues of copyright protection, but gave less attention on lossless data, distortion and speed. The traditional image encryption algorithms developed in the last few years may not be appropriate for different digital image formats because of data large size, real time constraint and unknown environment. In addition, some of them have been known to be insecure, the encryption methods allow to transform digital images into some unreadable form and hard to understand, and inverse transform feasibility of the encryption digital image to the target image. All these issues arise the need for reliable methods of encryption. [1] Genetic algorithms are basically a machine learning, heuristic search and optimization techniques based on the theory of Darwinian`s idea for the survival of the fittest and natural genetics. Cryptography is the practice and study of techniques for secure communication in the presence of adversaries [2, 3]. It is the study of sending messages methods in disguised form so that only intended recipients can read the message after removing the disguise. It provides an effective solution for protecting sensitive information in large number of application, including data security in personal, Internet, diplomatic and military communication etc., by implementing the processes of encryption and decryption. Generally, Genetic Algorithms contain three basic operators: Reproduction, Crossover and Mutation. Genetic Algorithm gains most of their searching power from reproduction and crossover. Various Genetic algorithm encryptions have been proposed. A. Tragha et al.[4,5], described a new symmetrical block ciphering system called ICIGA (Improved Cryptography Inspired by Genetic Algorithms) that generates a session key in a random process.


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Volume 1, Issue 6, December 2013 2. BACK GROUND

Several solutions have been proposed in this area. In 1993, for the first time, a paper by Spillman [6,7] presented a genetic algorithm based approach for the cryptanalysis of substitution cipher and another for the cryptanalysts of a knapsack cipher. This paper explored the possibility of random type search to discover the key (or key space) for a simple substitution cipher. In the same year, Mathew [8] used an order based genetic algorithm for cryptanalysis of a transposition cipher. In 2006, Garg [9] indicated that the efficiency of genetic algorithm attack on knapsack cipher can be improved with variation of initial entry parameters and Nalini [10] compared the attack of SDES using Optimization Heuristics technique and GA based techniques. The results showed that GA based approach minimize the time complexity. In 2008 Garg [11] explored the use of memetic algorithm to break a simplified data encryption standard algorithm.

3. THE PROPOSED METHOD The proposed method block diagram is shown down in Figure (1). Loading an Image

Determining the size

if H&W mode 8 ≠ 0

Mutation operation

Cross over operation

H = H + (8-(H mod 8)) W = W + (8-(W mod 8))

Dividing an image into set of blocks size (8*8)

Selecting 2 string randomly one vertical another horizontal

Decrypting Block Dividing the selecting strings into 2 parts and swapping between them Decrypting Image Getting new string

Cross over operation

Mutation operation

Encrypting Block

Encrypting Image

Figure 1: Block Diagram of the Proposed Method The proposed algorithm has proved to provide high protection to the images data from illegal intrusions. It is also fast in the process of encryption and decryption. Moreover, the decryption process does not cause any loss of image data, and it has the ability to deal with different format of images, as will

4. EXPERIMENTAL ANALYSIS This paper proposes a new approach for images security. The proposed algorithm will increase the efficiency of the algorithm in terms of computation time required and complexity to attack. It uses the concept of pseudorandom


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number generator and genetic algorithms to increase the complexity of key by increasing the irregularity of the key. The proposed algorithm is applied on different sizes and types of images. The test of images employed here showed positive result. The implementation of the above encryption algorithm was carried out using MATLAB, in core2duo of 2.66 GHz machine. The decryption algorithm takes between 76 and 100 micro seconds to get executed as shown in Table (1). Table 1: Images Properties Original image

Type

Dimension s

Size before encryption

Size after encryption

a

tif

259 * 194

140 KB

138.4 KB

b

bmp

2050*1153

145 KB

143.8 KB

c

jpg

255*255

12KB

12 KB

a. Security analysis To measure the strength of the encryption system against illegal decryption trials, researchers select two strings randomly from one block that have very good correlation properties. To decrypt one block, they have to guess 16 bit with possible number and to decrypt the whole image. However, according to the method presented in this paper, the possible number of guesses is , so an image has dimensions of 256*256 it is possible to have encryption result. The strength is the most essential feature that a good quality encryption algorithm should possess. If the encryption algorithm is unable to prevent all types of attack including statistical and brute force attacks, it will not be sufficient for protecting the data. Many experiments are carried out for defining the competency of the proposed technique. In this part, the proposed technique is applied on the images which have different formats and sizes. b. Statistical Analysis A statistical analysis has been madeby calculating the histograms, the entropy, the correlations and differential analysis for the plain image and the encrypted image to prove the strength of the proposed algorithm. After testing various images, it appears that the intensity values of encryption and decryption are good. 4.2.1. Histogram Analysis An image histogram is very important feature in analysing images. Figures (1-3) present histograms of RGB colours for the original, encrypted, and decrypted image of Lena. It is obvious that histogram of the encrypted image is nearly uniform and significantly different from the histogram of the original image. It does not give any clue to employ any statistical analysis attack on the encrypted image. Figures (2-4) confirms that statistical attacks based on the histogram analysis can’t give any clue to break the algorithm as all the statistical information of the original image are lost after the encryption. 4.2.2. Correlation of the Two Adjacent Pixels The recent statistical analysis is called adjacent pixels correlation. This method involves calculating three adjacent pixels correlation for each plain cipher image: vertically, horizontally, and diagonally. Figure (5) is the horizontal relevance of adjacent elements in image before and after encryption. It shows significant reduction in relevance of adjacent elements.


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Web Site: http://www.ipasj.org/IIJIT/IIJIT.htm Email: [email protected] ISSN 2321-5976

Figure 2: Original Image of Lena and Its RGB Histogram

Figure 3: Encrypted Image of Lena and Its RGB Histogram

Figure 4: Decrypted Image of Lena and Its RGB Histogram

Figure 5: Correlation analysis of two horizontally adjacent pixels


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IPASJ International Journal of Information Technology (IIJIT) A Publisher for Research Motivation ........


Web Site: http://www.ipasj.org/IIJIT/IIJIT.htm Email: [email protected] ISSN 2321-5976

5. CONCLUSION In this paper presented a digital image encryption algorithm based on bit-level using genetic algorithm. From the above results the proposed algorithm, can classified as more efficient, loss less and high speed algorithm. It is also a high level security algorithm

References [1] Gamil R.S. Qaid , Sanjay N. Talbar, “Encrypting Image by Using Fuzzy Logic Algorithm,” International Journal of Image Processing and Vision Sciences (IJIPVS), Vol-2, Iss-1, PP.2278 – 1110, 2013. [2] Rasul Enayatifar and Abdul Hanan Abdullah, “Image Security via Genetic Algorithm,” International Conference on Computer and Software Modeling IPCSIT vol.14, pp. 198-203, Singapore 2011. [3] David E Goldberg, “Genetic algorithms in search, optimization and machine learning,” Addision- Wesley Pub.Co.1989. [4] Menzes A. J., Paul, C., Van Dorschot, V., Vanstone, S. A., “Handbook of Applied Cryptography,” CRS Press 5th Printing; 2001. [5] National Bureau Standards, “Data Encryption Standard (DES),” FIPS Publication 46; 1977. [6] Spillman R,Janssen M, Nelson B and Kepner N, “Use of Genetic Algorithm in Cryptanalysis of Simple Substituion Cipher,” Cryptologia, Vol.17, No.4, pp. 367-377, 1993. [7] Spillman R, “Cryptanalysis of Knapsack Ciphers using Genetic Algorithms,” Cryptologia, Vol.17, No.4, pp. 367377, 1993. [8] Methew, R.A.J., “The use of genetic algorithms in cryptanalysis,” Cryptologia, 7(4),187-201, April1993. [9] Garg Poonam “Genetic algorithm Attack on Simplified Data Encryption Standard algorithm,” International journal Research in Computing Science, ISSN1870-4069, 2006. [10] Nalini, Cryptanalysis of Simplified data encryption standard via Optimization heuristics, International Journal of Computer Sciences and network security, vol 6, No 1B, Jan 2006 [11] Garg Poonam, “Memetic Algorithm Attack on Simplified Data Encryption Standard Algorithm,” proceeding of International Conference on Data Management, February 2008, pg 1097-1108. [12] Ankita Agarwal, “Secret Key Encryption Algorithm Using Genetic Algorithm,” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 4, pp. (216 -218), April 2012.


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