Cloud computing is a powerful, flexible, cost efficient platform for providing consumer IT services over the Internet. However Cloud Computing has various level of risk because most important information is maintained and managed by third party vendors, which means harder to maintain security for user’s data .Steganography is one of the ways to provide security for secret data by inserting in an image or video. In this most of the algorithms are based on the Least Significant Bit (LSB), but the hackers easily detects it embeds directly. An Efficient and secure method of embedding secret message-extracting message into or from color image using Artificial Neural Network will be proposed. The proposed method will be tested, implemented and analyzed for various color images of different sizes and different sizes of secret messages. The performance of the algorithm will be analyzed by calculating various parameters like PSNR, MSE and the results are good compared to existing algorithms.

1. A SECURE STEGANOGRAPHY APPROACH FOR
CLOUD DATA USING ANN ALONG WITH
PRIVATE KEY EMBEDDING
Dr.R.Kiran Kumar D.Suneetha*
Assistant Professor,CSE Dept Research Scholar
Krishna University, Krishna University,
Machilipatnam,AndhraPradesh,India Machilipatnam,A.P,India
kirankreddi@gmail.com sunithadavuluri8@gmail.com
Abstract: Cloud computing is a powerful, flexible, cost
efficient platform for providing consumer IT services
over the Internet. However Cloud Computing has
various level of risk because most important
information is maintained and managed by third party
vendors, which means harder to maintain security for
user’s data .Steganography is one of the ways to provide
security for secret data by inserting in an image or
video. In this most of the algorithms are based on the
Least Significant Bit (LSB), but the hackers easily
detects it embeds directly. An Efficient and secure
method of embedding secret message-extracting
message into or from color image using Artificial
Neural Network will be proposed. The proposed
method will be tested, implemented and analyzed for
various color images of different sizes and different
sizes of secret messages. The performance of the
algorithm will be analyzed by calculating various
parameters like PSNR, MSE and the results are good
compared to existing algorithms.
Keywords: Artificial Neural Network, Steganography,
PSNR, MSE
I.INTRODUCTION
In cloud computing environment, maintaining
security for data is the one of the vital parameter. For
that we have different approaches like cryptography
techniques, steganography techniques and
watermarking hiding strategies. Still those
techniques are suffered with some major problems
because data is maintained by a third party from
different places at different locations. So it is
necessary to have some novel methods which can
have the capability to embed the data securely. For
this in the proposed algorithm Steganography
technique is utilized for maintaining of data secrecy.
Steganography is a technique which can hide the data
in an image. The hiding information is any format
like audio,video,plain text file. The word
Steganography is comes from the Greek words
"Stegos" meaning "cover" and "Grafia" meaning
"written work" signifying it as "invisible writing".
Steganography is a novel technique which is used for
hidden message. The proposed technique uses spatial
domain environment for embedding secret message
into a single cover image using Artificial Neural
Networks to enhance the level of security for data.
The Organization of the paper is as follows. In
section 2 the related work is discusses. In section 3
materials and methods are discussed. In section 4 our
proposed method is described. Finally the results are
presented in section 5.
II. RELATED WORKS
In Suneetha D et.al’s [1] has proposed a new
algorithm using LSB based image steganography.In
this approach secret data is embed in the
combination of pixels. The secrete message is
converted into binary is in the form of 0 and 1. For
hiding 0 bit use some combination of two bits and for
1 use another combination of bits. Hence it is a
typical process for hackers to retrieve the data from a
image. Results are provided with high security, good
quality in stego image with acceptable PSNR values.
In Kiran Kumar R. et.al [2] has proposed a new
technique for embedding secret message. In which
first we identify the edge pixels using canny edge
detection algorithm. Next we identify the Fibonacci
edge pixels from an edge based image. Results are
International Journal of Computer Science and Information Security (IJCSIS),
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ISSN 1947-5500

2. evaluated and compared with various existing
algorithms and identify the improvement in PSNR
and MSE Values
In Kiran .R et.al’s [3] proposed a novel
steganography technique which increases the
capability of hiding of data securely. In this approach
First read the original cover image then divide the
original image into 9 equal parts for storing huge
amount of data. Then apply edge detection
algorithms and LSB substitution algorithm for each
partitions of an cover image to select the secret pixels
for data hiding. Later use encryption algorithm to
convert original message into cipher text and obtain
the key. Finally obtained secret key is hiding in the
selected pixels of an image. In this the amount of
secret message hiding is gradually increases and it
shows a better PSNR values.
In Suneetha .D [4] proposed a new approach in
spatial domain environment for hiding secret
messages in different parts of a cover image . This
method helps to embedding secret data with
minimum noise in the cover image.
In Siddharth Singh et.al’s [5] ha s proposed a novel
approach using DCT coefficients. In this approach a
sequence generator and android transform techniques
are used to hiding secret data securely. This
algorithm is well suited for JPEG image extension
files and it provides better PSNR values and also the
image quality is good when compared to other
existing algorithms.
In Sadeq AlHamouz et.al’s [6] has proposed a new
approach based on the neural network concept back
propagation. In this approaches two images are used
one is secret images and the cover images, both are
color images. The algorithm uses two different
phases one is data embedding process and other one
is data extracting process. The hiding bit positions are
calculated using Fibonacci linear feedback shift
register. The experimental results are compared with
several exciting algorithms that high PSNR value is
achieved with good quality of the image and more
processing time.
III. MATERIALS AND METHODS
3.1. Artificial neural network approach
In this proposed algorithm we use one of the
technique of neural networks i.e. cascaded feed
forward neural network along with it Levenberg
Marquardt training algorithm. The cascade feed
forward neural networks are similar to feed forward
networks. The cascaded feed forward networks
consist of several layers. Every layer has a
subsequent connection with other layers. The first
layer has a connection from the network input. Each
and every layer has the connection with the previous
layer.
The function newcf is used to create cascade forward
networks. For example consider a five layer network
which has the connection from one to five layers
respectively from one to five and also it have the
connection from input layer to all the five layers.
The importance of additional layer is to improve the
speed of the entire network
IV. PROPOSED METHOD
The proposed method here came to increase the
security level, reducing the embedding and extracting
time and reducing the noisy level of a cover image
after embedding the secret message.
Phase1: Embedding the secret message:
The procedure for data embedding is shown in
Figure1.
Figure 1: Data Embedding block diagram
This phase can be implemented by executing the
following sequence of steps:
Cover Image
Extract into RGB
Components
Trained using Cascaded
Obtain the private
random key
Read the
Encrypt and
obtain key
Divide the key
into three parts
Data Hiding
First secret
message is
Second secret
message is hiding in
Third secret
message is
Stego image
International Journal of Computer Science and Information Security (IJCSIS),
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3. 1. Get the color cover image
2. Divide the color image into three color
components and obtain the size of the each color
components (x1: number of rows; y1 number of
columns).
3. Read the secret data in to text file and encrypt
using AES Encryption algorithm and obtain the key.
4. Obtain the size of the key and divide into three
parts based on the key size (x2: size of the secret key)
.
5. The three separated color components are given to
cascade feed forward network and trained using
LMA algorithm and the shuffle position are obtained.
6. Generate 3 random private keys from the three
color shuffled position.
These keys can be implemented applying the
following formulas:
Kxred=ceil (rand (1, rand (1, floor(x2/3)))*x1);
Kyred=ceil (rand (1, rand (1, floor (x2/3)))*y1);
Kxgreen= ceil (rand (1, rand (1, floor (x2/3)))*x1);
Kygreen=ceil (rand (1, rand (1, floor (x2/3)))*y1);
Kxblue=ceil (rand (1, rand (1, floor(x2/3)))*x1);
Kyblue=ceil (rand (1, rand (1, floor (x2/3)))*y1);
7. First secret message is placed in Red component of
the color image at the positions of the red color
private key.
8. Second secret message is placed in green
component of the color image at the positions of the
green color private key.
9. Third secret message is placed in Blue component
of the color image at the positions of the blue color
private key.
10. The image after hiding the secret data and the
embedding three color components into a single
image and it is called as stego image
Phase 2: Extraction of secrete message
The obtained stego image as input for the receiver
side. At the receiver side the reverse operation is
performed to decrypt secret key and secret message.
V. EXPERIMENTAL RESULTS AND
ANALYSIS
The proposed algorithm is used to hide secret data in
the selected pixels, which meets all the requirements
in perception and robustness and its produce very
good results. he images are taken from the data set
http://sipi.usc.edu/database/. We have used different
color images of different sizes with various length
size messages for justifying the process this is shown
in Figures 2 to 5.
Covering image before embedding message
Red Color Component
Green Color Component
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4. Blue Color Component
Figure 2: Original image (with size 512*512*3)
Figure 3: Holding Image (message length=69)
Holding Red Component
Holding Green Component
Holding Blue Component
Covering image before embedding
message
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5. Red Color Component
Green Color Component
Blue Color Component
Figure 4: Original image (with size 512*512*3)
Holding Green Component
Holding Red Component
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6. Figure 5: Holding Image (message length=35)
5.1 Calculation of PSNR (Peak Signal to Noise
Ratio) and MSE (Mean Square Error)
The Peak Signal to Noise Ratio and Mean Square
Error are two important evaluation measurements for
calculating image quality ,for estimating noise ratio
in the stego image and those two factors are used to
differentiate original cover image with stego image.
The PSNR is used as a quality measurement between
the original cover image or input image and a
processed stego image. If the value of PSNR is high
the quality of stego image is also good.The MSE is
used to calculate the noise error ratio in between the
original cover iamge and the processed stego image.
PSNR=10 log10(MAXi2)/MSE (1)
MSE=ΣM,N[I1(m,n)-I2(m,n)]2/M*N (2)
5.2 Comparison Tables:
This section contains comparison for previous
algorithms and proposed artificial neural network
along with private key embedding algorithm.
It clearly is seen that calculated values show some
significant decrement which suggests that the
proposed algorithm is slightly better than the
previous approaches. MATLAB tools are used for
evaluating results of the output image.
TABLE 1: PSNR Values and MSE Values Obtained
Cover
Image(512*512)
PSNR MSE
Peppers 69.85 0.0043
House 63.54 0.0017
Female 70.69 0.0046
TABLE 2: PSNR Comparison with other Algorithms
Author Technique PSNR
Siddharth et.al’s Discerte Wavelet
transform (DWT)
41.54
Suneetha .D. et al’s LSB based
Embedding
43.04
Proposed Algorithm Artificial neural
network along with
private key
embedding
70.69
VI. CONCLUSION
In this proposed approach a secure artificial neural
network along with the private key embedding
algorithm is used to embed embed secret message
into a multiple componemts of a single cover image
and obtains a high quality stego image with less noise
ratio. The quality of stego image is obtained in terms
of PSNR and MSE values. This approach can be used
for improving embedding capacity level of high
quality stego images.
REFERENCES
1. Suneetha and Kiran Kumar “A Novel Algorithm for Enhancing
the Data Storage Security in Cloud through Steganography” ACST
ISSN 0973-6107 Volume 10, Number 9 (2017) pp. 2737-2744.
2. Suneetha and Kiran Kumar “ Data Hiding Using Fibonacci
EDGE Based Steganography for Cloud Data” International Journal
of Applied Engineering Research ISSN 0973-4562 Volume 12,
Number 16 (2017) pp. 5565-5569.
Holding Blue Component
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
91 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

7. 3. Suneetha and Kiran Kumar “ Enhancement of Security for
Cloud Data using Partition based Steganography”Interntation
conference on recent trends “.
4. Suneetha and Kiran Kumar “A Novel Approach For Data
Security In Cloud Environment Using Image Segmentation And
Image Steganography” Ijcsi International Journal Of Computer
Science Issues, Vol. 9, Issue 3, No 1, PP 131-139, May 2017 .
5. Siddharth Singh and Tanveer J. Siddiqui “A security enhanced
robust steganography algorithm for datahiding,”IJCSI International
Journal of Computer Science Issues, Vol. 9, Issue 3, No 1, PP 131-
139, May 2012 .
6. Reyadh Naoum, Ahmed Shihab, Sadeq AlHamouz” Enhanced
Image Steganography System based on DiscreteWavelet
Transformation and Resilient Back-Propagation” IJCSNS
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0302, April-2015.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 6, June 2018
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ISSN 1947-5500