Top Banner
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME 50 PUBLIC KEY STEGANOGRAPHY USING LSB METHOD WITH CHAOTIC NEURAL NETWORK Adel A. El-Zoghabi 1 , Amr H. Yassin 2 , Hany H. Hussien 3 1 Head, Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University 2 Lecturer, Electronics & Communication, Engineering Department, Alexandria Higher Institute of Engineering and Technology 3 PHD students, Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University ABSTRACT The art of information hiding has been around approximately as long as the need for secret communication. Steganography, the concealing of information, get up early on as an extremely useful method for covert information transmission. Steganography is the art of hiding secret image within a larger image, this is in contrast to cryptography, where the existence of the secret image is not disguised, but the content cover image is well obscure. The goal of a steganographic method is to minimize the visually apparent and statistical differences between the cover data and a stego-image while maximizing the size of the payload. This paper explains about how a secret image can be hidden into an image using least significant bit insertion method with chaotic map as public stego- key insertion along with chaotic neural network for merging methods. Keywords: Chaotic Map, Chaotic Neural Network, Data Hiding, Public key, Steganography. 1. INTRODUCTION Several digital services require more consistent security in storage and transmission for digital images transferring. Attributable to the rapid growth of the internet, especially in the digital world, the security of digital images has become more important and attracted much attention. The propagation of multimedia technology in our society has promoted the digital images to play a more significant role than the traditional texts, which request earnest protection of users' privacy for all applications. Public key encryption and steganography techniques of digital images are very essential and should be used to avoid opponent attacks from unauthorized access [1] [2]. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME: www.iaeme.com/IJCET.asp Journal Impact Factor (2014): 8.5328 (Calculated by GISI) www.jifactor.com IJCET © I A E M E
12

Public key steganography using lsb method with chaotic neural network

May 25, 2015

Download

Technology

iaeme iaeme

Public key steganography using lsb method with chaotic neural network
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

50

PUBLIC KEY STEGANOGRAPHY USING LSB METHOD WITH CHAOTIC

NEURAL NETWORK

Adel A. El-Zoghabi1, Amr H. Yassin

2, Hany H. Hussien

3

1Head, Department of Information Technology, Institute of Graduate Studies and Research,

Alexandria University 2Lecturer, Electronics & Communication, Engineering Department, Alexandria Higher Institute of

Engineering and Technology 3PHD students, Department of Information Technology, Institute of Graduate Studies and Research,

Alexandria University

ABSTRACT

The art of information hiding has been around approximately as long as the need for secret

communication. Steganography, the concealing of information, get up early on as an extremely

useful method for covert information transmission. Steganography is the art of hiding secret image

within a larger image, this is in contrast to cryptography, where the existence of the secret image is

not disguised, but the content cover image is well obscure. The goal of a steganographic method is to

minimize the visually apparent and statistical differences between the cover data and a stego-image

while maximizing the size of the payload. This paper explains about how a secret image can be

hidden into an image using least significant bit insertion method with chaotic map as public stego-

key insertion along with chaotic neural network for merging methods.

Keywords: Chaotic Map, Chaotic Neural Network, Data Hiding, Public key, Steganography.

1. INTRODUCTION

Several digital services require more consistent security in storage and transmission for

digital images transferring. Attributable to the rapid growth of the internet, especially in the digital

world, the security of digital images has become more important and attracted much attention. The

propagation of multimedia technology in our society has promoted the digital images to play a more

significant role than the traditional texts, which request earnest protection of users' privacy for all

applications. Public key encryption and steganography techniques of digital images are very essential

and should be used to avoid opponent attacks from unauthorized access [1] [2].

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)

ISSN 0976 – 6375(Online)

Volume 5, Issue 9, September (2014), pp. 50-61

© IAEME: www.iaeme.com/IJCET.asp

Journal Impact Factor (2014): 8.5328 (Calculated by GISI)

www.jifactor.com

IJCET

© I A E M E

Page 2: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

51

Chaos theory is considered to be brand new between sciences, and has been showed to be

showed to be the most powerful fields in presence. Chaos become at the most interested research in

physical systems, which make it implemented in many fields such as computer science, robotics,

economics, and psychology. The chaos science able to absorbed the scientific information which

may change the science hub in the future. In chaos theory, the term “Chaos” is not an opposite of

absence of order but in fact have a very intelligent order in itself not quite obvious as ordered

systems [3] [4].Chaotic signals have several properties that make them attractive candidate for

communication, such as wideband spectrum, waveform does not accurately repeat itself, random-like

appearance [5].

In the steganography the secret information can’t be detectable, because, the secret data is

hidden in a cover file which has notrigger for eavesdropper's suspicion to discover [6]. Capacity,

robustness, undetectable, invisibility, security and resistance against attacks are the main criteria in

steganography. The steganography is a form of security through ambiguity, by hidden the write

message (stego – object) in another object (host file) [7].Also statistical properties of the file, before

and after information embedding should have minimum differences. The cover-object for

steganography could be image, video clip, text, music and etc.

Due to the redundancy of data in digital images, a little change in the information cannot be

detected by the naked eye, which makes the digital images a good covers for steganography. Several

methods are used for image steganography such as Discrete Cosine Transform (DCT), Discrete

Wavelet Transform (DWT), which known as transform domain, and Least Significant Bit (LSB),

which known as spatial domain. In the first two methods the pixels are transformed into coefficients,

and then the secret bits are embedded in these coefficients, while the third one, the secret bits are

embedded directly in LSB image pixels. In the transform domain, major robustness is provided

against changes and attacks, where special domain has high embedding capacity [7].

The least significant bit (LSB) insertion is one of the most widely used methods for

embedding a message in a digital image. Steganography involves hiding information so it appears

that no information is hidden at all. Therefore, it is expected that the person will not be able to

decrypt the information. An alteration of the least significant bit of the color value of some pixels in

an image will not change the quality of the image significantly. Therefore, a message can be sent

within an image using these bits [8].

There are three types of steganographic: pure steganography, secret key steganography and

public key steganography. A steganography system that doesn’t need any exchange information

between secret communications before sending the message is named pure steganography. This

system it’s not provide any security if the attacker know the embedding method. On the other hand in

secret key steganography, the system is similar to a symmetric cryptosystem, where the sender and

the receiver must have a secret key to share between them. Used in the embedding process. The

receiver can reverse the process and extract the secret message. Public key steganography is similar

to asymmetric cryptosystem, where two keys are needs to process. One of keys is private (secret) and

the other is public, which are store in a public database. The public key is used in the embedding

process. The secret key is used to rebuild the secret message [9].

Neural network can be used to design a real time data protection, and hiding schemes for its

complicated and time-varying structures. Due to the attractive properties of combination neural and

the sensitively to initial value conditions and parameters of chaotic maps, a chaos- based neural

network created a combination model called a chaotic neural network (CNN) which given a novel

and efficient way for data hiding [10].

This work presents a public key algorithm coupled with a chaotic neural network model to

embed online image data into LSBs image pixels with key, and de-embed with another key. This

algorithm improves efficiency and robustness that are not existed in many image steganography

algorithms. The rest of the paper network is organized as follows, in section 2 discusses background

Page 3: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

52

and related work in the field of ANN based steganography. In section 3, the proposed model is

produced. In section 4, the performance and security analyses are described in detail. Finally section

5, concludes the whole work.

2. RELATED WORK

Steganography has many useful applications especially in the forefront of the current security

techniques by the gorgeous evolution in computational power, and the increase in security

consciousness.

Nameer N. proposed a new steganographic algorithm based neural network for hiding

information [11]. The model hides a large amount of information into color BMP image. The

adaptive image filtering and adaptive non-uniform image segmentation with bits replacement are

been used on the appropriate pixels, which are selected randomly using a new concept defined by

main cases with sub cases for each byte in one pixel. The proposed model has four high security

layers to make it difficult to break. The learning algorithm has been used on the fourth layer of

security through neural network to increase the difficulties of the statistical attacks. The algorithm

can embed efficiently a large amount of information that has been reached to 75% of the image size

(replace 18 bits for each pixel as a maximum) with high quality of the output.

Aboul Ella H. introduced an efficient approach for protecting the ownership by hiding the iris

data based on neural network [12]. The model hide the iris data into a digital image for

authentication purposes based on inspired spiking neural networks, called pulse coupled neural

network (PCNN) which first applied to increase the contrast of the human iris image and adjust the

intensity with the median filter. The PCNN segmentation algorithm is used to determine the

boundaries of the human iris image by locating the pupillary boundary and limbus boundary of the

human iris for further processing. The PCNN model is a two-dimensional neural network, each

neuron in the network corresponds to one pixel in an input image, receiving its corresponding pixel’s

color information as an external stimulus. The PCNN model is comprised of four parts that form the

basis of the neuron. The first part is the feeding receptive field that receives the feeding inputs, the

second part is the linking receptive field that receives the linking inputs from the neighbor neurons,

the third part is modulation field, which the linking input added a constant positive bias, then it is

multiplied by the feeding input, and the last part is a pulse generator that consists of an output pulse

generator and a threshold spike generator. Experimental results clearly indicate that the model

represent the authentication of the ownership very accurately.

Imran Khan proposed an original stenographic method based on neural network. The

proposed model hides the information message into cover image. The model divided the cover image

into blocks of non-overlapping pixels which generates a set of edged regions using pixel value

differencing (PVD) method. The total message calculated and store in the first pixel of the stego-

image. The model applied Xor back propagation neural network learning model for the embedding

process with the cover image and the key. The adjusted weights and the diagonal coefficient values

selected pixels act as the key. Experimental results show that the proposed method hides information

in edged regions and maintains a better visual display of stego image than the traditional methods

[13].

Arun Rana introduced a new high capacity image steganography method based on kohonen

neural network and wavelet contrast. The proposed method adopts the character that human eyes are

not sensible to the dark and texture block to embed the secret information into blocks. The kohonen

network is trained according to the absolute contrast sensitivity of pixels present in cover image

through classify the pixels in different classes of sensitivity which act as a secret key. The training of

the neural network determines the amount of information carried by individual pixels. Data

embedding is performed in less sensitive pixels by LSB substitution method, which replaces the least

Page 4: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

53

significant bits of cover image with secret information that would be embedded. The Optimal Pixel

Adjustment Process (OPAP) implemented to obtain an optimal mapping function to reduce the

difference error between the cover and the stego-image, therefore improving the hiding capacity with

low distortions. Compared with DCT, the proposed method hides much more information and

maintains a better visual quality of stego-image. One the other hand, the secret key can be detect

[14].

Hieu Van Dang presented a new intelligent, robust and adaptive data hiding technique for

color images based on the combination of discrete wavelet transform (DWT), human visual system

(HVS) model and general regression neural network (GRNN). First, the RGB image is converted to

YCrCb image, and then the luminance component Y is decomposed by DWT. Wavelet coefficients

are then analyzed by a HVS model to select suitable coefficients for embedding the watermark. The

watermark bits are embedded into the selected coefficients by training a GRNN. At the decoder, the

trained GRNN is used to recover the watermark from the watermarked image. Experimental results

show that the proposed approach is robust in achieving imperceptibility in watermarking [15].

In 2013, RakeshGiri proposed a multilayer perceptron algorithm neural network for data

hiding. The model used the substitution method for embedding data without visibility. The proposed

method uses a multi-layered perceptron has a synaptic link structure with neurons between the layers

but no synaptic link among the layer itself. The updated weight of the neural network with back

propagation learning method is used for the embedding process. Results are observed through

different Media file as cover-objects and indicate that the system provides confidentiality and

integrity to the data during communication through open channels [16].

3. THE PROPOSED ALGORITHM

The chaotic system is rich in importance for it characterizes such as sensitivity to change

initial conditions and parameters, ergodicity, random behavior, and unstable periodic orbits with long

periods.

The Chebyshev chaotic map used as a public key method for embedding and de-embedding

with LSB insertion technique, and used with neural network to produce a combination of CNN,

based on a binary sequence generated from the Chebyshev chaotic maps, which the weight of

neurons are set in every iteration for generation the public key. The Chebyshev map possesses the

semi group property which lead to TsTr= TrTs. Here the Chebyshev polynomial map Tn: R→R of

degree p is defined using the following recurrent relation: The Chebyshev map is defined in Equation

(1) [17]: ����� � cos�. cos� ���� ����� � 2���� ��� � ������� Where, n > 2, T0(x) = 1, T1(x) = x.

The public key algorithm model is composed of the following three parts, which are Key

generation, Embed process, De-embed process, as follow:

3.1. Key Generation This public key model uses two kinds of keys, a public key, and a private key. The keys are

decided by the de-embedor.

• Alice (receiver), in order to generate the keys, does the following steps:

Step 1: Generates a large integer S.

Step 2: Selects a random number X∈[−1,1] and computes Ts(X).

Step 3: Alice sets her public key to (X,Ts(X)) and her private key to S.

(1)

Page 5: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

54

• Bob (Sender), in order to generate the keys, does the following steps:

Step 1: Bob (Sender), Obtain Alice’s authentic public key (X,Ts(X)).

Step 2: Generates a large integer r.

Step 3: Computes Tr(X),Tr·s(X) = Tr(Ts(X))

Step 4: Bob use the computed Tr(Ts(X)) for encrypt any image sent to Alice and his private key to r.

The whole process is shown in (Fig. 1).

Fig. 1: Generate Keys

3.2. Embed Process

In the embed process, we used the least significant bit (in other words, the 8th bit) of some or

all of the bytes inside an image is changed to a bit of the secret image. Digital images are mainly of

two types, 24 bit images and 8 bit images. In 24 bit images we can embed three bits of information in

each pixel, one in each LSB position of the three eight bit values. Increasing or decreasing the value

by changing the LSB does not change the appearance of the image; much so the resultant stego-

image looks almost same as the cover image. In 8 bit images, one bit of information can be hidden,

which will be done in our model based on the public key model generation through the following

steps:

Step 1: Reshape the keyTr(Ts(x)).

Step 2: Create a chaotic bit sequences bTrs(0), bTrs (1), bTrs (2) from the chaotic sequences Tr(1),

Tr(2), Tr(3), ….. Tr(m) through the generation scheme b(m), b(m), …… b(m-1), which is the binary

representation of Tr(m) for m=1,2,….M.

Step 3: Calculated the weight for WTrs as follows:

For i=0 to M, and J=0 to M

�_����� � �1 �� � � � �� �!, �� � 0�1 �� �!, �� � 10 �� � $ � %

Page 6: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

55

End

Step 4: Compute the secret image by merge each bit of the original secret image (O_img) with the

weight of WTrs as follows:

For i=0 to M, and J=0 to M &'(�') �!�*' � +_�!*�� , �_�����

End

Step 5: Read the cover image.

Step 6: Determine the size and the length of cover image.

Step 7:Determine the public stego-key (ste_keyr)of n used in LSB embedding process based the

computed Tr(X),Tr·s(X) = Tr(Ts(X)) , which determine the number of LSB bits to be substituted in

the secrets image to be embedding into the cover image.

Step 8: Compute the stegnographic image (S_img) by insert the secret image in each first component

of next pixels of the cover image based on public stego-key, as follows:

For i=0 to M, and J=0 to M &_�!* � �)-��.-/'��01�� , �)�2��) 3&'(�')�01�� , &)'*-4567 � 89�

End

Step 9: Sends the stegnographic image S = (Tr(x),S_img) to the receiver Alice.

The embed process can be shown in (Fig. 2).

Fig. 2: The Embed Process

3.3. De-embed Process In the de-embed process, the received image is then fed to the proposed de-embed model

algorithm based on Chaotic Neural Network weights for retrieve the original secret image.

Alice, to extract the original secret image (O_img) from the stegnographic image (S_img), does the

following:

Step 1: Uses her private key S to compute Ts·r = Ts(Tr(x)).

Step 2: Determine the public stego-key (ste_keys) of n used in LSB embedding process based the

computed Ts·r(X) = Ts(Tr(X)).

Step 3: Reshape the keyTs(Tr(x)).

Page 7: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

56

Step 4: Create a chaotic bit sequences bTsr(0), bTsr (1), bTsr (2) from the chaotic sequences Ts(1),

Ts(2), Ts(3), ….. Ts(m) through the generation scheme b(m), b(m), …… b(m-1), which is the binary

representation of Ts(m) for m=1,2,….M.

Step 5: Calculated the weight for WTsr as follows:

For i=0 to M, and J=0 to M

�_����� � �1 �� � � � �� �!, �� � 0�1 �� �!, �� � 10 �� � $ � %

End

Step 6: Extract the embed image (E_img) from the stegnographic image (S_img) based on public

stego-key (ste_keys), as follows:

For i=0 to M, and J=0 to M :_�!* � �)���255, �)�2��) 3&�01�� , 8 � &)'*-456<9�

End

Step 7: Recover the original secret image by uncombined each bit of the original image (O_img)

from the weight of WTsras follows:

For i=0 to M, and J=0 to M -��*��= &'(�'() �!�*' � :_�!*�� � �_�����

End

The de-embed process can be shown in (Fig.3).

Fig. 3: De-embed Process

4. EXPERIMENT AND TEST RESULTS

A series of experiments have been conducted for comparing stego image with cover results

requires a measure of image quality. This section discusses the efficiency of the proposed public key

technique such as capacity, Mean-Squared Error, Peak Signal-to-Noise Ratio, and Root-Mean-

Squared Error.

Page 8: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

57

We demonstrate the performance of our public key model in steganography technique and

compare it with F5, J-Stego models, with two standard 512 * 512 grayscale images, which are Lena,

and Boat images using some measures:

4.1. Cover image and Stego Image The cover image and the stego image for the two standard 512 * 512 grayscale images are

shown in (Fig. 3).

In the exposed (Fig. 4) both original and the stego-image is shown. Stego-image looks like

the original image which does not show any distortion. Thus the stego-image will not attract

attention towards itself. So it can be transferred to the recipient without displaying information

within itself.

Fig. 4: Cover & Stego image

4.2. Mean Square Error (MSE) The MSE represents the cumulative squared error between the stego-image and the cover

image, the lower the value of MSE, the lower the error. The block calculates the mean-squared error

using the following Equation (2) [18] [19]:

Where Xij: The intensity value of the pixel in the cover image, X ij: The intensity value of the pixel in

the stego image, M*N: Size of an Image. The MSE value is shown in (Table 1) and (Fig. 5).

(2)

Fig.3: Cover &Stego image

Page 9: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

58

TABLE 1: Comparison of MSE values for various Embedding Algorithms

Cover Image Public key model J-Stego [20] F5 [21]

Lena 0.1148 1.4438 1.3039

Boat 0.1152 1.3503 1.3294

Fig. 5: Comparison of MSE values for various Embedding Algorithms

As shown form (Table 1) and (Fig. 5), the best embedding algorithms value MSE value is

the public key model, which implies that the algorithms is generates a stego-image that will be

immune to statistical steganalysis. It has been analyzed that Lena as a cover image have given the

best least MSE value.

4.3. Peak signal to noise ratio (PSNR) Its compute the peak error signal to noise ratio between the stego-image and the cover image.

The ratio is used as a quality of measurement between the cover and a stego-image. The block

computes the PSNR using the following in Equation (3) [18] [22]:

In this Equation L, is the maximum value of the cover image. The higher of the PSNR value,

the better the quality of the reconstructed image. The PNSR value is shown in (Table 2) and (Fig. 6).

TABLE 2: Comparison of PSNR (DB) values for various Embedding Algorithms

Cover Image Public key model J-Stego F5

Lena 63.59 33.36 36.94

Boat 63.48 35.67 36.23

(3)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Public key model J-Stego F5

MS

E V

alu

e

Embedding Algorithms

Lena

Boat

Page 10: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

59

As shown form (Table 2), and (Fig. 5) the obtained PSNR for the proposed public key model

is higher than other works by a remarkable difference, which means that the quality degradations

could hardly be perceived by a human eye. It has been analyzed that Lena as a cover image have

given the best PSNR value.

4.4. Root Mean Square Error (RMSE) Root Mean Square Error (RMSE) is calculated by getting the square root of the mean square

error (MSE)(MSE)which measures the average sum of thesaurus in each pixel of the stego-image.

The RMSE can be calculated as following in Equation (4) [23].

A low value of RMSE means a lower distortion in the stego-image [24].The RMSE value is

shown in (Table 3).

TABLE 3: Comparison of RMSE values for various Embedding Algorithms

Cover Image Public key model J-Stego F5

Lena 0.3388 1.2016 1.1419

Boat 0.3354 1.1620 1.1529

According to the experimental results in (Table 1, 2, 3), we can conclude that our public key

model a high value for PSNR and a low value for MSE, and RMSE, which make it a great approach.

5. CONCLUSION

Fig. 6: Comparison of PSNR values for various Embedding Algorithms

Steganography is an effective way to hide sensitive information. In this paper we have used

the LSB Technique for insertion with chebyshev chaotic map as public stego-key for embedding and

De-embedding, and chaotic neural network weight based on chebyshev chaotic map for merging

with secret image. The chaos system is highly sensitive to initial values and parameters of the

(4)

0

10

20

30

40

50

60

70

Public key model J-Stego F5

PS

NR

Vla

lue

Embedding Algorithms

Lena

Boat

Page 11: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

60

system. The image hiding quality of our public key is discussed and compared to other existing

algorithms (J-Stego, and F5) using some measures. The obtained results insure the work’s robustness

and high secrecy, compared with other works. The obtained high PSNR value, less MSE and RMSE

values, between cover and stego images, prove the introduced method to answer the needs of today's

secure communications.

REFERENCE

[1] Varsha Bhatt, Gajendra Singh Chandel, Implementation of new advance image encryption

algorithm to enhance security of multimedia component, International Journal of Advanced

Technology & Engineering Research (IJATER), Vol. 2, Issue 4, 2012, 13-20.

[2] Ravi Shanker, MHD. Rizwan, Manish Madhava, Image Encryption Techniques: A Critical

comparison, International Journal of Computer Science Engineering and Information

Technology Research (IJCSEITR), Vol. 3, Issue 1, 2013, 67-74.

[3] S. H. Strogatz, (Nonlinear dynamics and chaos, Preseus Books Publishing, LLC, 1994).

[4] J. Gleick, (Chaos: Making a New Science, Viking, New York, 1987).

[5] Ali Mohammed Noori Hasan, Lwaa Faisal Abdul Ameer, Implementation of Digital Chaotic

Signal Generator with an Efficient Cross-Correlation in Wireless Communications,

International Journal of Advancements in Computing Technology, Vol. 1, No. 1, 2009,

18-23.

[6] S. Channali, A. Jadhav, Steganography an art of hiding data, International Journal on

computer science and engineering, 3, 2009, 137-141.

[7] Zaidoon Kh. AL-Ani, A.A. Zaidan, B.B. Zaidan, Hamdan. O. Alanazi, Overview: Main

Fundamentals for Steganography, Journal of computing, Vol. 2, Issue.3, 2010, 158-165.

[8] M. S. Shashidhara, A. Pandurangan, Suhasini CHV, Data hiding and Retrieval of Encrypted

file in Images/Videos using ANN Method, Int. J. Advanced Networking and Applications,

Vol. 2, Issue 2, 2010, 544-549.

[9] Hamid. A. Jalab, A.A Zaidan, B.B Zaidan, New Design for Information Hiding with in

Steganography Using Distortion Techniques, International Journal of Engineering and

Technology (IJET), Vol. 2, No. 1, 2010, 72-77.

[10] Harpreet Kaur, Tripatjot Singh Panag, Cryptography using Chaotic Neural Network,

International Journal of Information Technology and Knowledge Management, Vol.4, No. 2,

2011, 417-422.

[11] Nameer N. EL-Emam, Embedding a Large Amount of Information Using High Secure

Neural Based Steganography Algorithm, International Journal of Information and

Communication Engineering 4:2, 2008, 95-106.

[12] Aboul Ella Hassanien, Ajith Abraham, Crina Grosan, Spiking neural network and wavelets

for hiding iris data in digital images, Soft Computing, A Fusion of Foundations,

Methodologies and Applications, Special Issue on Bio-Inspired Information Hiding, Vol. 13,

Issue.4, 2008, 401-416.

[13] Imran Khan, An efficient neural network based algorithm of steganography for image,

International Journal of Computer Technology and Electronics Engineering (IJCTEE) Vol. 1,

Issue 2, 2011, 63-67.

[14] Arun Rana, Nitin Sharma, Amandeep Kaur, Image steganography method based on kohonen

Neural Network, International Journal of Engineering Research and Applications (IJERA),

Vol. 2, Issue.3, 2012, 2234-2236.

[15] Hieu Van Dang, A Perceptual Data Hiding Technique for Color Image Protections,

University of Manitoba, Department of Electrical & Computer Engineering, Proceedings of

the 2012 Graduate Students Conference, GRADCON, 2012.

Page 12: Public key steganography using lsb method with chaotic neural network

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 9, September (2014), pp. 50-61 © IAEME

61

[16] Rakesh Giri Goswami, Sitendra Tamrakar, Evolution of Data Hiding By Neural Network and

Retrieval or Encrypted Image, Text, Audio and Video Files, International Journal of

Emerging Technology and Advanced Engineering, Vol. 3, Issue.5, 2013, 153-158.

[17] Ljupco Kocarav ,Shingao Lian, Chaos-based Cryptography theory algorithms and

applications , Studies in Computational Intelligence, Vol. 354, 2011.

[18] Hossein Sheisi, Jafar Mesgarian, Mostafa Rahmani, Steganography: Dct Coefficient

Replacement Method and Compare With Jsteg Algorithm, International Journal of Computer

and Electrical Engineering, Vol. 4, No. 4, 2012, 458- 462.

[19] Rengarajan Amirtharajan, R. Anushiadevi, V. Meena, Concealed to Protect and Protect to

Conceal: A Conserved Stego Image, Research Journal of Information Technology, Volume 5,

Issue 2, 2013, 209-217.

[20] T. Zhang, X. Ping, A Fast and Effective Steganalytic Technique against JSteg-like

Algorithms, Proc. 8th ACM Symp. Applied Computing, ACM Press, 2003, 307-311.

[21] A. Westfeld, high capacity despite better steganalysis (F5-a steganographic algorithm),

Proceedings of the Fourth International Workshop on Information hiding, 2137, 2001,

289-302.

[22] Rengarajan Amirtharajan, R. Anushiadevi, V. Meena, Concealed to Protect and Protect to

Conceal: A Conserved Stego Image, Research Journal of Information Technology, Volume 5,

Issue 2, 2013, 209-217.

[23] Mukesh Garg, A.P. Gurudev Jangra, An Overview of Different Type of Data Hiding Scheme

in Image using Steganographic Techniques, International Journal of Advanced Research in

Computer Science and Software Engineering, Volume 4, Issue 1, 2014, 746-751.

[24] Shuchi Sharma, Uma Kumari, A High Capacity Data-Hiding Technique Using

Steganography, International Journal of Emerging Trends & Technology in Computer

Science (IJETTCS), Volume 2, Issue 3, 2013, 288 – 292.

[25] Nagham Hamid, Abid Yahya, R. Badlishah Ahmad and Osamah M. Al-Qershi, “An

Improved Robust and Secured Image Steganographic Scheme”, International Journal of

Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 3,

2012, pp. 22 - 33, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472.

[26] Sonali Patil, Kapil Tajane and Janhavi Sirdeshpande, “Analysing Secure Image Secret

Sharing Schemes Based on Steganography”, International Journal of Computer Engineering

& Technology (IJCET), Volume 4, Issue 2, 2013, pp. 172 - 178, ISSN Print: 0976 – 6367,

ISSN Online: 0976 – 6375.

[27] Shamim Ahmed Laskar and Kattamanchi Hemachandran, “Steganography Based on Random

Pixel Selection for Efficient Data Hiding”, International Journal of Computer Engineering &

Technology (IJCET), Volume 4, Issue 2, 2013, pp. 31 - 44, ISSN Print: 0976 – 6367,

ISSN Online: 0976 – 6375.