An ideal steganographic scheme in networks using twisted payload

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 10 | Oct-2013, Available @ http://www.ijret.org 254
AN IDEAL STEGANOGRAPHIC SCHEME IN NETWORKS USING
TWISTED PAYLOAD
Logesh R1
, M Hemalatha2
, A Ramalingam3
, Kanimozhi K4
1
M.Tech Scholar, 2
Assistant Professor, 3
Associate Professor,
1, 2, 3
Department of Information Technology, Sri Manakula Vinayagar Engineering College, Puducherry, India
4
Researcher, Puducherry, India,
logeshr@outlook.com, hemalathamohanraj@gmail.com, a.ramalingam1972@gmail.com, mail4kanimozhi@gmail.com
Abstract
With the rapid development of network technology, information security has become a mounting problem. Steganography involves
hiding information in a cover media, in such a way that the cover media is not supposed to have any confidential message for its
unintentional addressee In this paper, an ideal steganographic scheme in networks is proposed using twisted payload. The
confidential image values are twisted by using scrambling techiques.The Discrete Wavelet Transform (DWT) is applied on cover
image and Integer Wavelet Transform (IWT) is applied to the scrambled confidential image. Merge operation is done on both
images and Inverse DWT is computed on the same to get the stego image. The information hiding algorithm is the reverse process of
the extracting algorithm. After this an ideal steganographic scheme is applied which generates a stego image which is immune against
conventional attack and performs good perceptibility compared to other steganographic approaches.
Index Terms: Network security, Steganography, Discrete Wavelet Transform, Integer Wavelet Transform, Modified
Arnold Transform, Merge Operation, Quality Measures
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1. INTRODUCTION
The continuing improvements in computer technologies and
the increase in Internet usage are responsible for the increasing
popularity of network-based data transmission. In the field of
network security, several techniques are being developed to
overcome unauthorized attacks and to protect the secret
information during transmission. Network security problems
can be categorized roughly into four areas: secrecy,
authentication, non-repudiation and integrity control. Secrecy
concerns with keeping the information away from the
unauthorized users, which means unauthorized users, cannot
be able to read and/or understand the information on transit.
There are mainly two techniques to achieve secrecy. They are:
cryptography and Steganography.
Steganography is used to securely transmit information in
open networks. Steganography is an important aspect of
security in communications. Digital communication has
become an essential part of infrastructure now-a-days, a lot of
applications are Internet based and in some cases it is desired
that the communication be made secret. Steganography
provides to hide the secret information and make
communication undetectable. The main goal of Steganography
is higher capacity and security of the confidential message.
In Steganography the secret information is hidden inside
another file without degrading the quality of that file such that
the intruder will not suspect any communication that is
happening. Steganography is one such means of achieving
security by hiding the data to be communicated within a more
innocuous data. The carrier file or cover file can be an image,
audio, video or text file. Steganography is used to hide the
secret information so that no one can sense the information.
2. LITERATURE STUDY
Review of related work has been conducted on an ideal
steganographic scheme in network using twisted payload.
Nowadays, Steganography has become the focus of research
for copyright protection. Fei Peng et.al., [1] presented a new
reversible data hiding algorithm based on integer transform
and adaptive embedding. This allows embedding more data
bits into smooth blocks while avoiding large distortion
generated by noisy ones, and thus enables very high capacity
with good image quality. Using combination of IWT and LSB,
reversible data hiding provides ideal solution. The technique
allows one to embed data in host image, exactly reconstructed
from the marked content. Vijay Kumar et al.,[2] proposed a
copyright protection scheme that combines the Discrete
Wavelet Transform (DWT) and Discrete Cosine Transform
(DCT).

__________________________________________________________________________________________
Here first DCT coefficient of secret image and cover image is
extracted and then applied DWT coefficients on both
separately. Two different secret keys are used for hiding of
extracted features of DCT coefficients in the features of cover
image. Weiqi Luo et.al.,[3] presented Edge Adaptive Image
Steganography Based on LSB Matching Revisited. He used
LSB and threshold based algorithm and achieved good visual
quality and high security.
Fawzi Al-Naima et al.,[4] proposed a modified high capacity
image steganography technique that depends on wavelet
transform with acceptable levels of imperceptibility and
distortion on the cover image with high levels of overall
security.
Sumanth Sakkara et al.,[5] has presented proposed method
that uses the secret information as a text message which is
embedded in a color image. The existing methods hide the
information using constant bit length in integer wavelet
coefficients. This paper uses variable bit length based on
integer wavelet coefficients to hide the data in a particular
positions using secret key by LSB substitution method. Hence
this algorithm increases the embedding capacity of the text
message and obtained stego image is imperceptible for human
vision. Gandharba Swain et.al [6] presented a quick review of
network security and steganography and also discussed
classification of network security techniques. He also
emphasized on steganography techniques also.
This paper is prearranged in the subsequent section.
Methodology of our proposed technique is explained in
section 3. Section 4 begins our proposed model. Testing and
quality measures discussed in section 5. Performance analysis
is discussed in section 6. Result analysis is illustrated in
section 7. Conclusion is discussed in section 8. Finally
references are given in the last section.
3. METHODOLOGY
3.1 Preprocessing
The preprocessing performs a variety of basic operations to
eliminate known distortion from the image being compared.
Pre-processing methods use a small neighborhood of a pixel in
an input image to get a new brightness value in the output
image. Histograms are functions describing information
extracted from the image. The histogram function is defined
over all possible intensity levels. For each intensity level, its
value is equal to the number of the pixels with that intensity.
3.2 Pixel Value Adjustment
The gray scale cover image and confidential pixel intensity
values vary from zero to 255. During the confidential hiding
process the intensity values of cover image may exceed
lower and higher levels which results in difficulty to
retrieve the confidential information at the destination.
Hence the cover image pixel intensity values are limited
to lower 15 and upper 240 instead of zero and 255.
3.3 Discrete wavelet transform
DWT are applied to discrete data sets and produce discrete
outputs. DWT eliminates the „blocking‟ artifacts that deprive
the reconstructed image of the desired smoothness and
continuity. Wavelets convert the image into a series of
wavelets that can be stored more efficiently than pixel blocks.
Discrete wavelet transforms map data from the time domain to
the wavelet domain. The result is a vector of the same size.
When applying discrete wavelet transform on an image, four
different sub-images are obtained as follows : LL
(Approximation Band): A coarser approximation to the
original image containing the overall information about the
whole image. It is obtained by applying the low-pass filter on
both x and y coordinates. HL (Vertical Band) and LH
(Horizontal Band) : They are obtained by applying the high
pass filter on one coordinate and the low-pass filter on the
other coordinate. HH (Diagonal Band) : Shows the high
frequency component of the image in the diagonal direction. It
is obtained by applying the high-pass filter on both x and y
coordinates.
3.4 Integer wavelet transform
In discrete wavelet transform, the used wavelet filters have
floating point coefficients so that when we hide data in their
coefficients any truncations of the floating point values of the
pixels that should be integers may make the loss of the hidden
information which may lead to the failure of the data hiding
system.
Integer Wavelet Transform is a Non linear transform having a
structure of lifting scheme and as its rate has less distortion.
The performance value is similar to DWT. Wavelet transforms
that map integers to integers allow perfect reconstruction of
the original image. Integer wavelet transform make an integer
data set into another integer data set. The use of such wavelet
transform will mainly address the capacity and robustness of
the information hiding system features.
3.5 Scrambling based on Modified Arnold Transform
Image scrambling is an important method of image
encryption. Its main purpose is to make the target image
scrambled so that no one is able to find the true meaning of the
image by using human visual system (HVS) or computer
system. To transform a meaningful image into a meaningless
or disordered image, enhance the security which in turn
enhances the power to resist the invalid attack.
The Arnold transform [7] of the matrix and then a new matrix
can be obtained in order to achieve image scrambling
processing. Set the image pixel coordinates. N is the order of

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the image matrix, i, j € (0, 1, 2, N−1) and the Arnold transform
is defined as equation as
i′
j′
=
1 2
1 1
i′
j′
Mod N (1)
It can easily be seen that the original Arnold transformations
given by equation (1) can be modified to produce a sequence
of Arnold transformations as given in equation (2) which
representing modified Arnold transform equation.
x′
y′
=
i i + 1
1 1
x
y Mod N (2)
where i ∈ 1,2,3 … . , The above transform is one-to-one
correspondence, the image can do iteration. The Iteration
number can be used as a secret key for mining the confidential
image. This transformation gives more security and robustness
to our algorithm.
3.6 Merge Operation
Add the wavelet co-efficient of both images, due to merge
operation Alpha multiplier gives deep depth value to hide the
secret image. This approach is capable of achieving more
security, imperceptibility, and certain robustness. All the
pixels of a image in spatial domain are multiplexed by
embedding strength factors alpha or beta. Since alpha and beta
are chosen, such that payload is not predominantly seen in the
stego image.
Stego image looks very similar to original cover image and
produces results in the statistical evaluation. The stego image
is obtained using the twofold transform technique and by
applying merge operation, such as logical operation,
arithmetic operation. The arithmetic operations of
multiplication, division, addition and subtraction have been
combined in different ways to achieve a better merge effect.
4. PROPOSED WORK
In this section, an ideal steganographic scheme in networks
using twisted payload is presented. The proposed method
creates high quality stego image thereby increasing security.
This proposed method can achieve high embedding capacity
and acceptable image quality of stego-image with excellent
PSNR value. The capacity of the proposed algorithm is
increased as the only approximation band of secret image is
considered.
In this scheme, the characteristics of the both integer wavelet
transform and discrete wavelet transform is proposed. The
diagrammatic representation of information hiding and mining
model was shown in Fig-1 and Fig-2. Information hiding
process and information mining process will be discussed in
section 4.1 and 4.2.
4.1 Information Hiding Process
 Read the cover image and secret image.
 Convert the pixel values of cover image and secret image
into a grayscale cover and confidential image.
 Apply PVA on cover image.
 Apply Modified Arnold transform on Confidential image
by using secret key to get a scrambled confidential image.
 Apply two fold transforms technique into cover grayscale
image and confidential gray scale image.
 By apply merge operation on both images to get stego
image.
Fig-1: Information Hiding Model [ C.I – Cover Image, M.O
– Merge Operation , CF.I - Confidential Image, S CF.I
– Scrambled Confidential Image, ST.I – Stego Image, IDWT –
Inverse Discrete Wavelet Transform]
4.2 Information Mining Process
 Receive the stego image.
 Perform a twofold transform at the level of both stego
image and known cover image using merge operation.
 Separate the wavelet coefficients and take inverse
transform of the merged image to reconstruct the
scrambled confidential image.
 By applying Modified Arnold transform on scrambled
confidential image by using secret key to recover the
original confidential t image.
 The hidden confidential image will be correctly
recovered.
C.I
M.O
IDWT
S CF.I
C.I
DWT
S CF.I
IWT
ST.I
PVA
CF.I
KEY

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Fig-2: Information Mining Model [ CF.I – Confidential
Image, M.O – Merge Operation , C.I – Cover Image, S
CF.I – Scrambled Confidential Image, ST.I – Stego Image,
IIWT – Inverse Integer Wavelet Transform]
5. TESTING AND QUALITY MEASURES
A standard sample images of different sizes of cover image
(image without secret image) and confidential image (image to
be hidden into cover image) is selected for the analysis of the
performance. Here a set of image with different size is hidden
into cover image, first set is 512x512 of confidential image
into 512x512 of cover image and second set is 256x256 of
confidential image into a 512x512 of cover image.
MATLAB is a high performance language for technical
computer, integrates computation, visualization and
programming in an easy way to use environment. One of the
reasons of selecting this is to evaluate the performance of the
statistical method. The proposed method is implemented by
using Matlab R2010a and 7.10 version.
5.1 Testing
The images from image databases are being tested. Images are
collected from database such as SIPI and University of
Washington. In order to prove an ideal steganographic scheme
in networks using twisted payload is effective, the proposed
method is tested and validated over a range of 20 different
standard gray scale images of different sizes including
Android.jpg, Nest.png, Train.jpg, Blue.jpg, Girl.jpg as the
cover image and Eagle.png, Monkey.png, Droplet.png,
Blue.jpg, Girl.jpg as the confidential image.
Fig-3 shows their corresponding histogram analyzation of
statistical steganography process. It shows that histogram plot
of cover image and stego image is same as well as histogram
plot of confidential image and recovered confidential image
are same. As we seen the stego image given nice invisibility
and quality.
5.2 Quality Measures
The measurements of image quality measures not only reduces
the image perceptibility but also enhances the robustness to
conventional attacks. PSNR and MSE are used to measure the
distortion between the original cover image and the stego
image.
The other Image quality measures, are Normalized Cross
Correlation and Structural Content, are taken for the
experiment. The image quality parameters with its
corresponding formula are used in this study which is
illustrated below.
1).The larger the value of Mean Square Error (MSE) then the
image quality is poor. MSE is defined in eqn (3).
MSE =
1
MN
xj,k − x′j,k
2
N
k=1
M
j=1
(3)
2).If Peak Signal to Noise Ratio (PSNR) values is small then
the image is in poor quality. PSNR is defined in eqn (4)
PSNR = 10
log10 255 2
MSE
dB (4)
3).The larger the value of Normalized Cross Correlation
(NCC) then the image is in poor quality. NCC is defined in
eqn (5).
NCC = xj,k − x′
j,k
1
Xj,k
2N
K=1
M
j=1
N
k=1
M
j=1
(5)
4).The larger the value of Structural Content (SC) then the
quality of the image is poor. SC is defined in eqn (6).
SC = (
N
k=1
M
j=1
xj,k)2
/ (
N
k=1
M
j=1
x′j,k)2
(6)
6. PERFORMANCE ANALYSIS
If there are more similarities between the cover image and the
stego-image, it will be harder for an attacker to find out that
the stego-image has important secret data hidden inside it .
This way, the secret data is more likely to travel from the
sender to the receiver safe and sound. Fig-4. Shows an ideal
steganographic scheme of sample output model
ST.I
M.O
IIWT
ST.I
DWT
C.I
DWT
SCF.I
CF.I
KEYC.I

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Table-1: The experimental results values of an ideal steganographic scheme with respect to their image quality measures
Performance analysis of these two fold transforms is done
based on parameters. PSNR is used to measure the quality of
the reconstructed image. The PSNR is used to measure the
distortion between an original cover image and stego image.
MSE is the mean square error representing the difference
between the original cover image x sized M x N and the stego
image x‟ sized M x N, and the xj,k and x‟j,k are pixel located
at the jth row the kth column of images x and x‟,
respectively.
Normalized Correlation coefficient (NCC) between recovered
confidential image and original confidential image, is used as
a metric for performance evaluation. The value of NCC lies
between -1 and +1. If two images are identical, then its value
will be +1, if they are completely opposite then its value will
be -1 and it will be 0 if images are completely uncorrelated.
Structural Content value ranges between 0 and 1. If value
close to one then it shows highest correspondence with the
original image The image quality measurement values is
compared with the other existing method, and shown in Table-
1.
7. RESULT ANALYSIS
This paper deals with secret communication in open
environment like internet. Steganographic method has many
challenges such as high hiding capacity and imperceptibility.
The main goal of steganography is to communicate securely in
such a way as to avoid drawing suspicion to the transmission
of a hidden data.
It has been observed that when the MSE increases, and this
affects the PSNR inversely. So, from trade-off it was found
that MSE decrease causes PSNR increase and vice-versa
PSNR is often expressed on a logarithmic scale in decibels
(dB). PSNR values falling below 30 dB indicate a fairly low
quality, i.e. distortion caused by embedding can be obvious.
From Table-1, it is observed that values are acceptable ranges
so the confidential image is hidden in the cover image using
twisted algorithm securely. PVA is also carried in this
technique to improve the PSNR value upto 2dB. However, a
high quality stego-image should strive for 40 dB and above.
Our results indicate that embedding process introduces less
perceptual distortion and higher PSNR.
Fig-3: Histogram plot of ideal steganographic scheme
(a) cover image, (b) stego image, (c) confidential image (d)
recovered confidential image
Table-1: The experimental results values of an ideal steganographic scheme with respect to their image quality measures
IMAGE
SETS
COVER
IMAGE
(C.I)
CONFIDENTI
AL IMAGE
(CF.I)
Same size (256 x256) for both C.I and
CF.I
C.I ( 512x512) and CF.I (256x256)
PSNR MSE NCC SC PSNR MSE NCC SC
Image
Set 1
Nest.png Monkey.png 45.9960 1.6349 0.9918 1.0165 51.6756 0.4421 0.9973 1.0055
Image
Set 1
Android.j
pg
Eagle.jpg 46.1907 1.5632 0.9940 1.0121 52.4941 0.3662 0.9985 1.0029
Image
Set 1
Train.jpg Droplet.png 45.2529 1.9400 0.9928 1.0146 51.0274 0.5133 0.9981 1.0037
Image
Set 1
Blue.jpg Cow.jpg 42.7445 3.4565 0.9937 1.0126 49.4781 0.7333 0.9986 1.0028
Image
Set 1
Girl.jpg Animal.png 42.0377 4.0674 0.9910 1.0182 47.1741 1.2464 0.9976 1.0048
[PSNR – Peak Signal Noise To Ratio, MSE – Mean Square Error, NCC – Normalized Cross Correlation, SC- Structured
Content]
C
O
U
N
T
PIXELVALUE
(a) (b)
C
O
U
N
T
C
O
U
N
T
C
O
U
N
T
PIXELVALUE
(c) (d)
PIXEL VALUE PIXELVALUE

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It is to be noted that PSNR ranging from 42dB to 52 dB means
that the quality degradations could hardly be perceived by a
human eye. Here the confidential message is hidden in an
image file in such a manner that the degradation in quality of
the carrier image is not noticeable. Thus the proposed method
allows users to send data through the network in a secured
fashion and it can be employed for applications that require
high-volume embedding with robust against attacks. The
steganography method may be further secured if the secret
message is compressed first and then encrypted. Finally
embed the obtained image inside the cover image. Here
confidential image is encrypted before hiding into cover image
for security purpose using scrambling algorithm. Modified
Arnold Transform used for scrambling the confidential image.
However, steganography can protect data by hiding it in a
cover object but using it alone may not guarantee total
protection. Thus, the use of encryption in steganography can
lead to „security in depth‟. The algorithm pre-adjusts the
original cover image in order to guarantee that the
reconstructed pixels from the embedded coefficients would
not exceed its maximum value. Hence the message will be
correctly recovered. Steganography systems do not need to
be robust; but they should satisfy high steganography capacity
and secret data imperceptibility.
Fig-4: An ideal steganographic scheme of sample output
model (a) cover image, (b) confidential image , (c) scrambled
confidential image, (d) Cover image DWT , (e) scrambled
confidential image IWT, (f) Stego image, (g) Recovered
scrambled confidential image, (h) Recovered Confidential
image.
8. CONCLUSION AND FUTURE WORK
With the rapid growth of numerous multimedia applications
and communications through Internet, secret image sharing
has been becoming a key technology for digital images in
secured storage and confidential transmission. Though
steganography is not implemented in wider ways but it can be
the best security tool. Main problem of today‟s world is to
secure their data confidentially, the techniques used currently
are not considered the best which can only be replaced by
steganography. Although the entire variety of steganography
techniques make available high imperceptibility, security and
robustness. It is not effortless to develop a method that
satisfies all these three needs because for being application
dependent may vary from one application to another
application.
In future, this technique also compares all possible
combination of multiple domains of cover image to increase
the security level. This technique will concentrate on
perfecting the visual effect of the stego image and the
robustness against the various attacks and also compare the
twisted techniques by using different wavelet families.

__________________________________________________________________________________________
REFERENCES
[1]. Fei Peng, Xiaolong Li, and Bin Yang , “Adaptive
reversible data hiding scheme based on integer
transform” , Elsevier, Signal Processing ,vol (92), pp:
54–62, 2012.
[2]. V .V. Das, R. Vijaykumar and Dinesh Kumar, “
Digital Image Steganography Based on Combination of
DCT and DWT ” , Springer, ICT,CCIS 101, 00.596-
601, pp: 596-601, 2010.
[3]. Weiqi Luo, Fangjun Huang and Jiwu Huang , “Edge
Adaptive Image Steganography Based on LSB
Matching Revisited” IEEE Transactions On
Information Forensics And Security, Vol. 5, No. 2, pp:
201-214, 2010.
[4]. Ali Al-Ataby and Fawzi Al-Naima , “A Modified High
Capacity Image Steganography Technique Based on
Wavelet Transform”, International Arab Journal of
Information Technology , vol 7 (4) , pp . 1-7 , 2010.
[5]. Sumanth Sakkara. M, Akkamahadevi D.H. , K.
Somashekar and Raghu k., “Integer Wavelet based
Secret Data Hiding By Selecting Variable Bit Length”,
International Journal of Computer Applications (0975 –
888) Volume 48– No.19, pp:7-11, 2012.
[6]. Gandharba Swain and Saroj Kumar Lanka, “A quick
review of network security and steganography”,
International Journal of Electronics and Computer
Science Engineering , ISSN-2277-1956/V1N2-426-
435, pp: 426-435
[7]. Minati Mishra , Ashanta Ranjan Routray and Sunit
Kumar, “High Security Image Steganography with
Modified Arnold‟s Cat Map” International Journal of
Computer Applications (0975 – 8887) Volume 37–
No.9, 2012.
BIOGRAPHIES
Mr. Logesh R received B.Tech degree in Computer Science
and Engineering from BCET, Karaikal. He is currently
pursuing M.Tech degree in Networking at Sri Manakula
Vinayagar Engineering College, Puducherry. He has presented
papers in national / international conferences. His areas of
interest are networking and steganography. Presently working
on how to solve network security problems using
steganography
Ms. M Hemalatha received B.Tech degree in Computer
Science and Engineering from BCET, Karaikal. She obtained
M.Tech degree in Network and Internet Engineering from
Pondicherry University. Presently she holds the position of
Assistant Professor of Information Technology at Sri
Manakula Vinayagar Engineering College, Puducherry. She
has published papers in national / international journals and
conferences. Her areas of interest are Ontology, Networking
and Image Processing.
Mr. A Ramalingam is pursuing his Ph.D at Pondicherry
Engineering College. He has 16 years experience. Presently
He is working as Associate Professor in Department of
Information Technology, Sri Manakula Vinayagar
Engineering College, Puducherry. He has published papers in
national / international journals and conferences. His areas of
interest are Genetic Algorithms, Networking and Image
Processing.
Ms. Kanimozhi K received B.Tech degree in Information
Technology from BCET, Karaikal. She obtained M.E degree
in Computer Science Engineering from Annamalai University
Chidambaram. Presently doing research on finding new
methods to overcome problems for secure communication Her
areas of interest are image processing, steganography. She has
published papers in national, international conferences and
international journals.

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