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Byline: Muhammad Saqib Javed and Muhammad Munwar Iqbal

ABSTRACT--This research article focuses on image processing and encryption techniques. Image encryption is very vast field and currently over World Wide Web and security is main issue. It presents the technique of image encryption by using cellular automata. The image is splitted in number of pixels block of pixels are created to be encrypted using blowfish algorithm and these encrypted blocks are considered afterwards as cells for cellular automata so that the rules on the image cell blocks are properly applied. The core theme is to double encrypt the image before transmission by using blowfish algorithm for effective communication. Decryption is done at the receiving end using inverse transformation which operates on backtracking procedure. Such technique of dual encryption leads us in achieving enhanced security under space and time constraint.

Keywords: Blowfish XOR Cellular Automata Cell Neighbors Random Number Generator.


The concept of cellular automata gives the clear understanding about its label which is Game of life". A cellular automation is actually a discrete mathematical model representing the cell matrix which operates on the states and the rules are applicable to cells after transformation.

The Information security carries much importance in very field of life. Especially the Military affairs and confidential business are very sensitive in this regard. To keep data away from the access of unauthorized users or to make it safe from being corrupted is called data security. Encryption is a very important security mechanism. The principle of its working is to scramble the information into unreadable information and then unscramble it for reading using a key. Encryption of the text is different from that of an image. Due to the intrinsic characters of images such as bulk data capacity and high redundancy encryption on image or video objects has its own requirements. Many algorithms provide different levels of security and it is based on how hard they are to break such as we use Blowfish encryption algorithm. If the cost required to break an algorithm is greater than the value of the encrypted data then the algorithm probably is considered safe. However modern high quality

image encryption methods have several loopholes and are subjected to extensive attacks by expert cryptanalyst. Thorough study and analysis between these techniques are needed to measure the performance and to choose the better one for the intended application [1]. For some applications speed of encryption may be the basic point of concern and for some other cases the security will be crucial.


The need to make our data more secured in every respect becomes strong with the technological advancement. There must be some way to make our data free from internal as well as external threats. Image Security under space and time constraint is the most demanding requirement. As far as encryption of the text is concerned it is quite easy as compared to encrypting images due to size constraint. Transformation of images is very much required to contain required number of pixels for real picture generation. The basic network security challenges for image security are described below:

Privacy and Confidentiality Issues

The confidentiality of sensed data goes far beyond the provision of a secure channel from the sensor node to some gateway node [2]. Such type of encryption and key distribution particularly provides key role in the fulfillment of better security level. Our focus is the privacy challenges which we face while collecting and disseminating data in the form of an image using cellular automata.

Integrity Issues

If the opportunistic sensing system provides anonymity to both nodes: ones which are associated with the task of communicating image data and the ones which submit reports then it gets difficult to ensure the integrity and reliability of information. If a participant misbehaves by falsifying data [2] it would be impossible to block that user since he has been provided full anonymity. Therefore the main challenge in this system would be to discover a solution which balances privacy with the integrity of data. However we can make use of the logics that automation provides us with different security algorithms.

Reliable Data Reading Issues

Image encryption is quite different from traditional text because of the reason that text consists of characters whereas image consists of pixels [8]. Therefore to deal with large number of pixels and make them integrate still remains the problem in previous works. The integration and making image data reliable is the main concern of the researchers. In this techno-advanced environment reliability of the data becomes more important: the adversary is no longer only a malicious outsider capturing a subset of [2] sensor nodes; now any user with a properly configured device can report fake data.

3.EXISTING SYSTEM OF IMAGE PROCESSING PresentlytheimageisconvertedusingFourier transformation and using coordinates of images to encrypt image overall. The cellular automation for image security system is shown in Figure 1.

The working idea for the proposed image encryption /decryption method is to transform the values of the pixels [9]. These values are transformed using data reformation and CA substitution. Data reformation keys are used to transform data whereas CA substitution is assigned to CA keys. CA Encryption/decryption scheme is described in this section first.


The current system resolves around the Cellular automata transformation with private key. This is not providing the effective level of security in terms of sending encrypted image. There must be a solution for addressing the problem to achieve the optimum level of security for image encryption and sending it to destination in any form such as jumbled image pixel group level encryption etc. The cellular automata allows us to use its cell considering rules for image placement in pixel group form and by applying rule the pixel group neighbor changed in every iteration in this way we can make the solution of the stated problem.


The proposed framework for image encryption and decryption based on cellular Automata and blowfish algorithm is shown in Figure 2.

The proposed system depends upon the replacement of the pixel values. These values are changed using a CA substitution with a CA key stream sequence. And this Cellular Automata sequence is generated using the CA evolution rules (XOR). The steps involved for CA evolution rules are very large in number. We double encrypt the image by applying blowfish first then we apply cellular automata rules on the cells containing a block of image pixels to produce a sequence of CA data encrypting and decrypting images. CA substitution works with integer values only such as arithmetic And/ Or logic operations and it simplifies the computation. The proposed security system for data in the form of images belongs to the framework called Dual Image Encryption using Cellular Automata and Blowfish Algorithm as shown in Figure 2.

This framework has been widely studied as far as cryptographic strengths and loopholes are concerned and it works as a basis for many contemporary encryption methods including Data Encryption Standard (DES) Advanced Encryption Standard (AES). The proposed image encryption method is unique and innovative in terms of secure image processing before communication.


The theme of the proposed framework is double encryption which means we encrypt the image of type jpeg twice; first by encrypting the image using blowfish algorithm then jumbling the image in the cells of cellular automata in order to make the image unreadable before communication. The blocks are equally scrambled across the image for applying blowfish algorithm on the random number generated by generator for each pixel block. After that the second level of encryption by bitwise XOR operation [5] is applied on scrambled image. The image converted in pixel is divided into blocks followed by block based shuffling using cellular automata transformation to produce the ciphered image for secure transmission as we achieved entropy of image in this procedure. The framework components are described below:

6.1 Image and Conversion Unit

Any image with different formats can be taken as an input image for conversion. The conversion unit takes the image as input and takes the necessary measures for image conversion based on the defined image format. The image taken as input by the conversion unit is shown in Figure 3. 6.2 Pixel Division Controller

It is the task of the pixel division controller to split the complete image of size NxN into pixels having each pixel separate RGB values. After this split pixels are gathered in equal number of pixel blocks as shown in Figure 4.

In order to utilize these for encryption using blowfish encryption algorithm the image transformed in pixels is shown by the formula below:Equation

6.3 Image TransformationThe Image is transformed in the transformation table containing pixel block representing parts of an actual image as shown in the Figure 6.

As shown in the Figure 6 the first cell represents pixel group formed a pixel block representing part of an image. Now in the proceeding steps we generate a random number for this pixel block by using generator in order to encrypt the pixel block using Blowfish encryption algorithm. The Splitted image is shown in Figure 7.

Here we see that this block contains total 9 pixels 3 pixels in each row. Now we take this block of pixel as a group pixel and use it encryption based on their RGB's or Hexadecimal values then we place these encrypted codes in the cells of the cellular automata to apply rules on it for dual encryption. Image transformation is done using following Pseudo code.

The above written will iterate until complete number of pixels of an image are shuffled. 6.4 Random Number Generator for Cells Representation The random number generator provides random number to pixel block every time. It works to generate unique identifier for pixel block since this is very difficult to contain each pixel RGB and apply algorithm on it. At this stage we already have our image pixel shuffled using the image described earlier where we actually XOR each pixel value and place all in transformation table.

6.5 Encryption Algorithm

The encryption algorithm now use the random number generated for each block of pixel for encryption process. The detailed algorithm is described below: The Blowfish algorithm complete flow representing the key generation and the F Functions with complete encryption rounds are shown in Figure 9.

The Blowfish is actually a symmetric block cipher that encrypts data in 8 byte (64 bit) blocks. There are two parts in this algorithm the first one is key expansion and the second one is data encryption. Key extension comprises of generating the initial contents of one array named as the Parray having eighteen 32 bit sub keys [4] along with four arrays named as the S-boxes each having size 256 by 32 bits from a key of at most 448 bits written as 56 bytes. The data encryption practices a 16 round Feistel Network.

6.6 Activation of Cells in Cellular Automata

The two dimensional space of automata is divided into cells.

Each of these cells has On/OFF states. The cells in the cellular automata are made active when occupied with the pixel blocks in order to make use of these active cells. Image is contained in the pixels and encrypted pixel blocks are taken in cell uniformly so make them shuffled for further encryption in order to accomplish dual encryption according to the theme of the paper. The cells in cellular automata are occupied with block codes received from blowfish algorithm using cellular array program.

6.7 Rules Definition of Cells in Cellular Automata

We are focusing on 2 dimensional cellular automata. The patterns are stored in cellular automata cells where we used uniform cellular automation on all cells of matrix 3x3 for cells shuffling in order to achieve our desired objective of making the block code taken through blowfish into transformed from in cellular automata. We are going to apply cellular automata on our cells containing encrypted pixel group codes in maximum 5 iterations for generations of cells placing in order to handle it for easy decryption. Next transition rule for defining the process of ageing cells is [6]: Cell: +-number : Cell operator number : InitState ;

Where Cell is a name of the cell the +-number is an integer increment which will be added in iteration considering the state of the cell referred to as new generation.


If range of C1 between 10-20 Place C1 value to C3

If range of C2 between 20-30 Place C2 value to C4

If range of C3 between 30-40 Place C3 value to C5

If range of C4 between 40-50 Place C4 value to C6

If range of C5 between 50-60 Place C5 value to C7

If range of C6 between 60-70 Place C6 value to C8

If range of C7 between 70-80 Place C7 value to C9

If range of C8 between 80-90 Place C8 value to C1

If range of C9 between 90-100 Place C9 value to C2

Initially the cellular automata of encrypted block code to occupy the cells of the cellular automata using uniform cellular automata transformation is given as:

Image Compilation (CA) = C1 + C2 + C3+ C4 + C5 + C6+ C7+ C8+ C9

Table 2: Cellular Automata Grid (First Generation)

C1=47###C2= 35###C3= 42



Table 3: After applying rules on cells (2nd Generation)

###C1=47###C2= 51###C3= 42



Table 4: After applying rules on cells (3rd Generation)###communication.

###C1=47###C2= 13###C3= 42



Table 5: After applying rules on cells (4th Generation)

###C1=47###C2= 13###C3= 42



Table 6: After applying rules on cells (5th Generation)

###C1=47###C2= 13###C3= 42



Similarly for fetching the cell values to default we apply inverse rule for back tracking the original codes to be used for decryption using blowfish where the decrypted random numbers are used to get the desired pixel blocks to be splitted into pixels for getting image back to its original shape.

Similarly for getting the default cell positions with values apply these rules in inverse. 6.8 Inverse Rules Transformation For the decryption of the dual encrypted image we apply inverse rules as shown in proposed framework and as applied through sequential flow in the proposed working of the image encryption using blowfish algorithm and cellular automata.


It is concluded that the procedure of double encryption of an image using the finite automata provides an improved and advanced system for image security. This approach combines image transformation and encryption techniques to make its working successful. It also uses the concept of even scrambling row column and block based pixels shuffling to minimize correlation. The double encryption is performed on the shuffled blocks of pixels using blowfish algorithm. This enforces security provided with cellular automata rules implementation support. The proposed system can have implementation in a multi-core and multiprocessor environment hence saving on computational time to make it safer for communication. The proper working of secure image processing functionality is verified by the use of technology for implementing first the image pixel division using some automated tool then the block formation of variable size of the pixels already divided. That to apply encryption algorithm on it in real time environment to make it proved as double encrypted image ready for communication.


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[5]. Rakesh S. Ajitkumar A. Kaller B. C. Shadakshari and B. Annappa. "Multilevel Image Encryption." arXiv preprint arXiv:1202.4871 (2012).

[6]. Hsu Chih-Yu et al. "Salt and Pepper Noise Reduction by Cellular Automata." International Journal of Applied Science and Engineering 9.3: 143-160 (2011).

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[9]. Krikor Lala Sami Baba Thawar Arif and Zyad Shaaban. "Image encryption using DCT and stream cipher." European Journal of Scientific Research 32 no. 1: 47-57 (2009).
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Publication:Science International
Article Type:Report
Date:Sep 30, 2014

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