International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 9 (2016) pp 6881-6885
© Research India Publications. http://www.ripublication.com
Improved Steganography using Enhanced K Strange Points Clustering
Terence Johnson
PhD scholar (Information Technology), AMET University, Chennai, India.
Assistant Professor, Dept. of Computer Engineering, Agnel Institute of Technology & Design, Goa, India.
Dr. Santosh Kumar Singh
Head, Dept. of Information Technology, Thakur College of Science and Commerce
Kandivali (E), Mumbai, Maharashtra, India.
Valerie Menezes
Assistant Professor, Dept. of Computer Engineering,
Agnel Institute of Technology & Design, Goa, India.
Edrich Rocha, ShriyanWalke, DikshaPrabhuKhorjuvekar, Sana Pathan
BE Students, Dept. of Computer Engineering, Agnel Institute of Technology & Design, Goa, India.
1
Stego-system: A stego-system in image steganography is the
one in which we can hide secret message such that no third
party will be aware of its existence [2]. The output image
from this process is known as stego-image and this image is
almost similar as the input image.
Encoder: The encoder is that part of the process who embeds
the secret message into the cover medium.
Decoder: The decoder is the one who receives the image
which contains the secret data.
Clustering: A group of similar object is known as a cluster
[10]. Clustering is the process which forms these clusters [5].
They are created based on color, size etc. These clusters
contain useful information [6]. We can get different sets of
data using clustering [7]. The objects in one cluster are similar
to each other and are different from the objects in other cluster
[8]. This approach is very useful in image steganography. In
this paper we perform clustering based on pattern matching
using color (RGB). We first divide the image into n clusters
based on color. After the process of clustering we select the
largest cluster and embed the secret message in that cluster
using steganography. This stego-image is then sent over some
channel to the receiver. On the other end, we apply the inverse
procedure wherein the input is the stego-image. After forming
the clusters the largest cluster is identified and the secret
message is extracted.
Abstract
Steganography is an act of hiding information. It uses a cover
medium to hide the secret information within itself. We
perform the act of steganography using clustering. In this
paper, we implemented the Enhanced K Strange Points
Clustering Algorithm to achieve steganography. We then
compare the results obtained with the K Means clustering
algorithm and find that our methodology of implementing
steganography works better with the Enhanced K Strange
Points Clustering Algorithm. LSB technique is used to hide
data in the cover medium. Any type of image can act as a
cover medium. We propose an improved scheme which
provides a better hiding capacity.
Keywords: Steganography, Clustering, LSB technique,
Steganography using Clustering, k-Means clustering and
Enhanced K Strange Points Clustering Algorithm.
Introduction
In this new era, most of the communication takes place
through the internet. This data which is transferred is not
secured and it can be attacked by any third party and
decrypted. Thus we can use steganography to hide the secret
data such that its existence cannot be detected. Steganography
is the process of hiding the data into the cover medium. It
hides the data in such a way that only the receiver knows the
existence of the secret message. In earlier days data was
hidden using wax tablet, writing tables, etc. Now, the data is
transmitted in the form of text, image, audio with the help of
the cover medium[1]. Before going deep into the core of each
algorithm let us take a moment to define the terms which
would be used to improve the readability, and make it easier
to understand how each algorithm works with regards to
others.
Enhanced K-Strange Clustering Algorithm
The Enhanced K-Strange Points Clustering Algorithm [3]
projected in this paper is about discovering strange points
which are maximally apart from each other. This algorithm
also addresses the effect on the running time due to choice of
two farthest points from the dataset. This algorithm first finds
the minimum Kmin from the dataset. It finds this value by
computing the Euclidean distance [9] between all the points
from the dataset. The algorithm now finds the second point is
farthest from Kmin. This point is represented as Kmax. This
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 9 (2016) pp 6881-6885
© Research India Publications. http://www.ripublication.com
algorithm improves the running time by first finding the
minimum from the dataset and then it finds another point
which is farthest from the minimum. This procedure avoids
the need of selecting the two strange points by calculating the
Euclidean distances between all the points from the dataset,
which thereby improves the performance of the algorithm. It
requires O(n) times for finding the Kmin and Kmax point.
Next the algorithm finds the third strange point from the
dataset which is maximally separated from the two strange
points. If the third strange point is closer to Kmin, then the
position of the third point is corrected using (1) by selecting a
middle point between the third strange point and Kmax and
this becomes the final strange point. However, if the third
strange point is closer to Kmax, then the position of the third
strange point is relocated by finding a middle point between
the third strange point and the Kmin and this new point
becomes the final third strange point[4].
3.
4.
5.
6.
Perform clustering (Enhanced K Strange) based on
color.
Select the largest cluster in which secret message is
to be hidden.
Hide the message in the selected image using LSB
steganography.
Transmit the stego-image over the selected channel.
Figure 1: Sender side process
The LSB Technique:
The least significant bit i.e., the 7th and 8th bit of an image
pixel is changed to a bit of secret message. When using 24 bit
image, one can store up to 3 bits in each pixel by changing 2
bits of each of the red, green and blue components.
As an example, suppose that we have three adjacent pixels (9
bytes) with the RGB encoding:
10010101
00001101
11001001
10010110
00001111
11001011
10011111
00010000
11001011
At the receiver side: Here we apply the inverse procedure
which is as follows:
1. Take the input image (stego-image).
2. Scan the image according to RGB (pattern
matching).
3. Perform clustering (Enhanced K Strange based on
color.
4. Select the largest which contains the secret message.
5. Perform extraction to get the secret message.
When a number 254 whose binary representation is 11111110
embedded into the least significant bits of this part of the
image, we get the following data sets after embedding:
10010111
00001111
11001011
10010110
00001111
11001011
10011111
00010000
11001011
Here only 3 bits needed to be changed according to the
embedded message. Only few bits in an image will need to be
modified to hide a secret message. Since there are 256
possible intensities of each primary color changing the LSB of
a pixel results in small changes in the intensities of the colors.
Figure 2: Receiver side process
Experimental Results
First we are going to implement k-Means algorithm and show
the outputs of sender and receiver side respectively.
We are going to perform the same operations as mentioned
earlier (introduction).
The image selected to perform the above technique is shown
in the figure below (input image):
Motivation
When we studied k-Means algorithm [4] we found out that kMeans doesn't converge for large datasets. During the
implementation we noticed that the algorithm is time
consuming and it takes many iterations to obtain the output.
The time complexity is directly proportional to the size of the
image. To reduce this factor we are using Enhanced K Strange
Points Clustering Algorithm, which reduces the time
complexity and the number of iterations.
Proposed Methodology
At the sender side: To hide the data he following steps are
followed:
1.
Take a color image which will act as a cover
medium.
2.
Scan the image according to RGB (pattern
matching).
Figure 3: Receiver side process An image selected for the
above proposed technique.
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 9 (2016) pp 6881-6885
© Research India Publications. http://www.ripublication.com
From the obtained output we select the largest cluster and
insert the message in the same. This is shown in the figure
below:
The Enhanced K-Strange Clustering Algorithm
The Enhanced K-Strange Points Clustering Algorithm runs
faster than the K Means clustering method for growing size
and higher dimensionality of data. The below table shows the
purity of the 2 methods which is a measure of the level to
which a cluster has items fitting to any lone cluster. From the
table it can be seen that not only does the Enhanced K-Strange
Points Clustering Algorithm run faster than the K Means
clustering method but in the process it also provides good
quality of clusters.
Clustering Algorithm
K Means
Enhanced K Strange
Purity (with Iris Dataset)
0.88
0.86
We implement the Enhanced K-Strange Clustering Algorithm
and show the outputs of sender and receiver side respectively.
The image selected to perform the above technique is shown
in the figure below (input image):
Figure 6: Receiver side process Snapshot of message
insertion into largest cluster.
The ASCII value of the message is converted into an 8 bit
binary number and the same is inserted into the selected
image. The message length is sent along with the message to
the receiver over some selected channel. Example: the
message length of 'helloooo' is 8 and hence we append the
message length(8) to the message '8helloooo'.
Figure 4: Receiver side process An image selected as input
for Enhanced K Strange.
The clusters formed for the above image is shown in figure
below:
Figure 7: Receiver side process The stego-image.
Message length is required to display only those many
characters of the message. The garbage value indicates that
there is no message inserted at that position inside the image.
Extraction is done using LSB technique where the last 2 bits
are selected from every pixel which forms an 8 bit and this is
converted back to the ASCII value and hence we get the
message.
The image received at the other end is shown in the figure 7
The clusters formed on the stego-image is shown in figure
below:
Figure 5: Receiver side process Output of Enhanced K
Strange Clustering at sender side.
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 9 (2016) pp 6881-6885
© Research India Publications. http://www.ripublication.com
The ASCII value of the message is converted into an 8 bit
binary number and the same is inserted into the selected
image. The message length is sent along with the message to
the receiver over some selected channel. Example: the
message length of 'be' is 2 and hence we append the message
length (2) to the message '2be'. Message length is required to
display only those many characters of the message. Figure 7
shows how a message is stored inside an image. The garbage
value indicates that there is no message inserted at that
position inside the image. Extraction is done using LSB
technique where the last 2 bits are selected from every pixel
which forms an 8 bit and this is converted back to the ASCII
value and hence we get the message.
The image received at the other end is shown in the figure
below:
Figure 8: Receiver side process Output of Enhanced K
Strange Clustering at receiver side along with message
extracted.
At the receiver end the stego-image is received and clustering
is performed and the same procedure takes place. In figure 8,
we can see that the received message is 'helloooo' which was
the input message that was inserted during the sending
process.
Comparison with K Means
The same was implemented using K Means and is shown
below for comparison with the proposed methodology.
The clusters formed for the above image is shown in figure
below:
Figure 11: Receiver side process The stego-image.
The clusters formed on the stego-image is shown in figure
below:
Figure 9: Receiver side process Output of K Means
Clustering at sender side.
From the obtained output we select the largest cluster and
insert the message in the same. This is shown in the figure
below:
Figure 12: Receiver side process Output of K Means
Clustering at receiver side along with message extracted.
At the receiver end the stego-image is received and clustering
is performed and the same procedure takes place. In figure 12,
we can see that the received message is 'be' which was the
input message that was inserted during the sending process.
Figure 10: Receiver side process Snapshot of message
insertion.
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International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 9 (2016) pp 6881-6885
© Research India Publications. http://www.ripublication.com
Conclusion
In this paper we implemented the Enhanced K Strange Points
Clustering Algorithm to achieve steganography. We then
compared the results obtained with steganography using K
Means clustering algorithm and find that our methodology of
implementing steganography works better with the Enhanced
K Strange Points Clustering Algorithm.
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