Preparation of Papers for ICIET Conferences: Embedded Data With Image Using Chaos Based Particle Swa

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

Preparation of Papers for ICIET Conferences: Embedded Data with Image using Chaos based Particle Swarm Optimization(PSO) 1G.

Dhivyakamatchi 2 K. Banupriya 3B. M. Nagarajan 3 Assistant Professor 1,2,3 Department of Computer Science Engineering 1,2,3 K.L.N. College of Engineering, Pottapalayam, Sivagangai 630612, India Abstract Data hiding is also known as data encapsulation or information hiding, it reduces system complexity for increased robustness .Proposed approach provides optimal solution for performing data hiding. The goal of data hiding technique is to embed the secret data into the cover image with minimum changes in the pixel values. Here the secret data is generated and embedded into the cover image by random function of MATLAB and various chaotic map approaches. Then the embedded image is send to the Particle Swarm Optimization technique (PSO).In this technique, optimal solution is achieved which is calculated based on the maximal Peak Signal to Noise Ratio (PSNR) value. So, the final stego image which is obtained has the minimal changes in the pixel values .In this Project, long running time will get reduce.

Keyword- Data hiding, information security, Genetic Algorithm, Particle Swarm Optimization, Chaos, chaos map __________________________________________________________________________________________________

I. INTRODUCTION Digital image processing is the process of applying algorithm to digital images. Various algorithm like secure hash function algorithm, advanced encryption Standard algorithm are available. It is a subfield of signals and systems but focus particularly on images. The most preferable example is Adobe Photoshop. It is one of the widely used applications for processing of digital images.

Digital images are electronic snapshots taken of a scene or scanned from documents such as photographs, manuscripts, printed texts and artwork the digital image is sampled and mapped as a grid of dots or picture elements .Nowadays, providing information security is one of the important issues in open networks. The leading information security techniques are data hiding, water marking and cryptography. The data hiding techniques is widely used in information security. The goal of data hiding technique is to embed the secret image into cover image with minimal changes. Each type of data has its own features; therefore different techniques should be used to protect confidential image data from unauthorized access. First techniques included invisible ink, secret writing using chemicals, templates laid over text messages, microdots, changing letter/word/line/paragraph spacing, changing fonts Images, video, and audio files provide sufficient redundancy or effective data hiding. The data hiding is important for the prevention of fraud detection, Data integrity, and self-correcting images. The data hiding techniques should have the features like robustness, invisibility security, undetectability.

In this paper, the application of data hiding based on the Particle swarm optimization (PSO) technique is compared with the genetic algorithm (GA).The comparisons prove the long running time is a disadvantage in the genetic algorithm which is overcome by particle swarm optimization technique.

II. SYSTEM OVERVIEW In our system, cover image which we selected is of different dimension. so image resizing is performed .The resized is of the dimension 256x256.The resizing process is according to user requirement. The data are generated by random function in MATLAB and chaotic maps. The generated data is embedded with pixel values of the cover image. The embedded image is then applied to optimization algorithms like Genetic algorithm and particle swarm optimization algorithms. PSNR and BER values are calculated for both algorithms. Depending upon PSNR values it will prove that the particle swarm optimization algorithm will reduce long running time problem.

All rights reserved by www.grdjournals.com

150

Preparation of Papers for ICIET Conferences: Embedded Data with Image using Chaos based Particle Swarm Optimization(PSO) (GRDJE / CONFERENCE / ICIET - 2016 / 025)

Fig. 1: System Overview

III. DATA EMBEDDING The mapping of one set into another is called embed. In our project, the data which is generated by maps and random functions is embedded into the Resized image Embedding is done by adding pixel value with data bits. This embedded image is used for optimization techniques.

A. Genetic Algorithm Optimization techniques have become an inevitable part of design and development activity in all major disciplines and are not restricted to engineering. These techniques can be applied to different disciplines. The GA optimization technique is based on the principles of natural selection and genetics (Haupt and Haupt 2004). GA lets a population composed of many different individuals develop specified selection rules.

Features of genetic algorithm are Population Initialization: Population is initialized using the extracted features. The features were divided in a particular range and the divided regions were encoded and considered as chromosomes. Selection Operation: In the selection operation for the number of populations generated the probability for selecting a feature is calculated.

For this probability calculation fitness value is calculated based on fisher criterion. Crossover Operation: New individuals were generated from the recombination of the existing individuals and the probability is calculated for each individual’s. Mutation Operation By inversing one bit in each part of an individual’s a child is created. Probability is calculated for the generated new child.

Termination stopping condition for the process is provided by identifying the convergence of the algorithm at which the maximum fitness is obtained after number of generations.

B. 1) 2) 3)

Procedure Step 1: In accordance with the purpose, the population is formed with a group of randomly created individuals.

Step 2: These individuals are evaluated in the population.

Step 3: The evaluation algorithm is provided by the system and gives the individuals a score according to the fitness function of the system.

4) Step 4: The two best individuals are selected based on fitness function. The higher the fitness value, the higher the chance of being selected.

5) Step 5: The individuals reproduce to form one or more individuals, after which the new individuals are randomly mutated.

6) Step 6: This process proceeds until a convenient solution has been found or a certain number of generations have passed, depending on the requirement of the system.

C. Chaotic Maps Chaos, founded by Lorenz in 1963, has been used in many disciplines even though chaos means confusion, disorder, turbulence, and disorganization, which seem to an organized description and systematic study appears to be the antithesis of harmony, order and bounder A small differencing the initial conditions of a chaotic system may generate very great differences in the output of the system. Data can be generated using Random function and chaotic maps .Random function is also used to generate initial value for the chaotic maps. Chaotic map is a map that exhibits some order of chaotic behaviour. xn+1 = ρ(x n )

All rights reserved by www.grdjournals.com

151

Preparation of Papers for ICIET Conferences: Embedded Data with Image using Chaos based Particle Swarm Optimization(PSO) (GRDJE / CONFERENCE / ICIET - 2016 / 025)

Different chaotic maps are available. In our system we used three kinds of maps. They are Chebyshev map, Logistic maps, Sinusoidal maps.

D. Chebyshev Map

Fig. 2: Chebyshev Map

Chebyshev map is symmetrical region map; it is used for security problems and neural networks. It is a kind of one dimensional map. Initially the first bit is generated randomly using randsrc function. Where the bits are generated between -1 to 1.

-1 Xn+1=cos(ρ cos xn) ρ>0 xn€(0,1) Data Bits using Chebyshev map 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

0

50

100

150

200

250

300

Fig. 3:

E. Logistic Map

Fig. 4: Logistic Map

All rights reserved by www.grdjournals.com

152

Preparation of Papers for ICIET Conferences: Embedded Data with Image using Chaos based Particle Swarm Optimization(PSO) (GRDJE / CONFERENCE / ICIET - 2016 / 025)

This logistic map generated bits between 0 and 1 which is a polynomial map. Initially the first bit is generated randomly using rand function. Where the bits are generated between 0 to 1.Next sequence bits are generated by using the following equation, Xn+1= ρ Xn(1- Xn) 0< ρ<= 4 Xnᵋᵋ€(0,1) Data Bits using Logistic map 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

50

100

150

200

250

300

Fig. 5:

F. Sinusoidal Map

Fig. 6: Sinusoidal Map

This sinusoidal map generated bits between 0 and 1 which is a polynomial map. Initially the first bit is generated randomly using randsrc function. Where the bits are generated between 0 to 1.Next sequence bits are generated by using the following equation, Xn+1=sin(∏ Xn) Xn€(0,1) Data Bits using Sinusoidal map 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0

50

100

150

200

250

300

Fig. 7:

All rights reserved by www.grdjournals.com

153

Preparation of Papers for ICIET Conferences: Embedded Data with Image using Chaos based Particle Swarm Optimization(PSO) (GRDJE / CONFERENCE / ICIET - 2016 / 025)

G. Random Function Random number generator is a computational or physical device designed to generate a sequence of numbers or symbols that can be reasonably predicted better than by a random change. Random generators have applications in gambling, statistical sampling, computer simulation, cryptography, completely randomized design and other areas where producing an unpredictable result is miserable.

Capacity 1000 2000 4000 8000

Random Function Step Number PSNR 5925 86,0173 12422 83,6725 25919 78,4380 54875 74,2650

BER 2.4x10-5 11x10-5 20x10-5 55x10-5

Fig. 8:

H. Previous Work Optimization techniques are used to solve some complex problems. There are some limitations to the optimization techniques used in solving complex problems. In this paper, the data hiding problem is optimized using genetic algorithm. In the genetic algorithm steps, single point crossover operator is applied and the mutation point is randomly selected. PSNR, which is a visual quality metric, is used as fitness function (Zhang et al. 2014). Row, column and layer information of image are used for generating the population. For 512 × 512 × 3 sized image, individuals consist of 20 bits (_log2512 × 512 × 3_ bits). The genetic algorithm issued to embed secret data into the best indices. Random function of MATLAB and chaotic maps, which are mentioned are applied for testing the randomness of the genetic Algorithm. The steps of the proposed method are given below: 1) Step 1: Obtain pixels of cover image 2) Step 2: Determine size of the individuals for m × n × k size image by using Equation.

X = log2 m × n × k.

3) Step 3: Generate × bits sized four random individuals using chaotic maps or random function of MATLAB. 4) Step 4: Calculate PSNR value for individuals in the population 5) Step 5: Store two individuals that have the best PSNR value.

6) Step 6: Generate four new individuals from these individuals according to genetic algorithm steps.

7) Step 7: Update population with new individuals.

8) Step 8: Obtain PSNR value after update.

9) Step 9: Store individuals that are over the threshold value.

10) Step 10: Remove repeated individuals.

11) Step 11: Repeat steps 4–10 until reaching the number of iteration or required error value.

I. Particle Swarm Optimization PSO system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.) This value is called pbest.

Another "best" value that is tracked by the particle swarm optimizer is the best value, obtained so far by any particle in the neighbors of the particle. This location is called lbest. When a particle takes all the population as its topological neighbours, the best value is a global best and is called gbest. The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest locations (local version of PSO).Acceleration is weighted by a random term, with separate random numbers being generated for acceleration toward pbest and lbest locations.

All rights reserved by www.grdjournals.com

154

Preparation of Papers for ICIET Conferences: Embedded Data with Image using Chaos based Particle Swarm Optimization(PSO) (GRDJE / CONFERENCE / ICIET - 2016 / 025)

J. Procedure The particle swarm algorithm begins by creating the initial particles, and assigning them initial velocities. It evaluates the objective function at each particle location, and determines the best (lowest) function value and the best location .It chooses new velocities, based on the current velocity, the particles' individual best locations, and the best locations of their neighbours .It then iteratively updates the particle locations (the new location is the old one plus the velocity, modified to keep particles within bounds), velocities, and neighbour .Iterations proceed until the algorithm reaches stopping criterion.

K. Performance Analysis PSNR is most commonly used to measure the quality of reconstruction of lossy embedding codes. image in this case is the original data, and the noise is the error introduced by embedding.

PSNR = 10log Max (CI2i,j) 1/mn ∑i m∑jn(CI i,j –SI i,j )2 The bit error rate (BER) is the number of bit errors per unit time. The bit error ratio (also BER) is the number of bit errors divided by the total number of transferred bits during a studied time interval.

BER= Total Number of Bit Changes Total Number of Bits CAPACITY 1000 2000 4000 8000

BIT ERROR RATE CHEBYSHEV LOGISTIC 2.6x10-5 2.2 x10-5 7.9x10-5 6.5 x10-5 -5 30x10 15 x10-5 58x10-5 41 x10-5

SINUSOIDAL 3.4x10-5 10 x10-5 32 x10-5 59 x10-5

Fig. 9:

IV. CONCLUSION In this paper the data hiding application is performed with the chaos embedded genetic algorithm and Particle swarm optimization.

The major disadvantage of a system based on genetic algorithms is long running time. In the proposed method, data is generated using random functions of MATLAB and chaotic maps. The obtained results of chebyshev, logistic and sinusoidal are better than random function of MATLAB. There are limitations in achieving effective results from the genetic algorithm such as determination of crossover, mutation techniques and end conditions. In this paper long running time problem can also be solved by using other Particle swarm optimization techniques with chaotic maps.

REFERENCES [1] Baykasoglu A (2012) Design optimization with chaos embedded great deluge algorithm. Appl Soft Comput 12:1055–1067 [2] Caponetto R, Fortuna L, Fazzino S, Gabriella M (2003) Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Trans Evol Comput 7:289–304 [3] El-Emam NN (2015) New data-hiding algorithm based on adaptive neural networks with modified particle swarm optimization. Comput Secure 55:21–45 [4] Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, New York [5] Jawad K, Khan A (2013) Genetic algorithm and difference expansion based reversible watermarking for relational databases.

J Syst Soft 86(11):2742–2753

All rights reserved by www.grdjournals.com

155

Preparation of Papers for ICIET Conferences: Embedded Data with Image using Chaos based Particle Swarm Optimization(PSO) (GRDJE / CONFERENCE / ICIET - 2016 / 025)

[6] Khan MK, Zhang J, Tian L (2007) Chaotic secure content-based hidden transmission of biometric templates. Chaos Solitons Fractals 32(5):1749–1759.

[7] Kannan HR, Nazer B (2014) A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm. Expert Syst Appl 41:6123–6130 [8] Kanso A, Own HS (2012) Steganographic algorithm based on a chaotic map. Commun Nonlinear Sci Numer Simul 17(8):3287–3302 [9] Kurban T, Civicioglu P, Kurban R, Besdok E (2014) Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl Soft Comput 23:128–143 [10] Lin CC, TsaiWH(2004) Secret image sharing with steganography and authentication. J Syst Softw 73(3):405–414 [11] Li X, Wang J (2007) A steganographic method based upon JPEG and particle swarm optimization algorithm. Inf Sci 177(15):3099–3109 [12] Schaefer R (2007) Foundations of global genetic optimization. Springer, Berlin [13] Yang D, Liu Z, Zhou J (2014) Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization. Commun Nonlinear Sci Numer Simul 19:1229–1246.

[14] Zhang M, Tong X (2014) A new chaotic map based image encryption schemes for several image formats. JSyst Softw 98:140– 154.

[15] ZhangW,MaK,YuN(2014) Reversibility improved data hiding in encrypted images. Sig Process 94:118-127.

All rights reserved by www.grdjournals.com

156