LSB Based Image Steganography for Information Security System

International Journal of Trend in
International Open Access Journal
ISSN No: 2456 - 6470
@ IJTSRD | Available Online @ www.ijtsrd.com
LSB Based Image Steganography
Professor,
Kayah State, Republic of t
ABSTRACT
Information hiding in a cover file is one of the most
modernized and effective ways for transferring secret
message from sender to receiver over the
communication channel. There are many
steganographic techniques for hiding secret message
in image, text, audio, video and so on. Image
Steganography is also one of the common methods
used for hiding the information in the cover image. In
this research work, the secret message is hidden in a
cover image file using image steganography. LSB is
very efficient algorithm used to embed the
information in a cover file. The LSB based image
steganography with various file sizes is analyzed and
illustrated their results. Bitmap (*.bmp) image is used
as a cover image file to implement the proposed
system. The detail Least Significant Bit (LSB) based
image steganography is introduced. In this paper, the
new embedding algorithm and extracting algorithm
are presented. While embedding the secret message in
a cover image file, the starting embedded pixel is
chosen according to public shared key between sender
and receiver. The original cover image and embedded
image with secret message are analyzed with PSNR
values and SNR values to achieve security. The
resulting embedded image shows the acceptable
PSNR and SNR values while comparing with the
original cover image. The proposed system can help
the information exchanging system over
communication media.
Keywords: Image Steganography, LSB, Data hiding,
information security, PSNR
I. INTRODUCTION:
Significant advancements in digital imaging during
the last decade have added a few innovative
dimensions to the field of image processing [1]. The
word steganography is derived from the Greek words
stegos meaning cover and grafia meaning writing [2].
International Journal of Trend in Scientific Research and Development (IJTSRD)
International Open Access Journal | www.ijtsrd.com
6470 | Volume - 3 | Issue – 1 | Nov – Dec 2018
www.ijtsrd.com | Volume – 3 | Issue – 1 | Nov-Dec 2018
LSB Based Image Steganography for Information Security System
Aung Myint Aye
, University of Computer Studies, Loikaw,
Republic of the Union of Myanmar, Myanmar
Information hiding in a cover file is one of the most
modernized and effective ways for transferring secret
message from sender to receiver over the
communication channel. There are many
steganographic techniques for hiding secret message
in image, text, audio, video and so on. Image
Steganography is also one of the common methods
used for hiding the information in the cover image. In
this research work, the secret message is hidden in a
over image file using image steganography. LSB is
very efficient algorithm used to embed the
information in a cover file. The LSB based image
steganography with various file sizes is analyzed and
Bitmap (*.bmp) image is used
cover image file to implement the proposed
system. The detail Least Significant Bit (LSB) based
image steganography is introduced. In this paper, the
new embedding algorithm and extracting algorithm
are presented. While embedding the secret message in
over image file, the starting embedded pixel is
chosen according to public shared key between sender
and receiver. The original cover image and embedded
image with secret message are analyzed with PSNR
values and SNR values to achieve security. The
ng embedded image shows the acceptable
PSNR and SNR values while comparing with the
original cover image. The proposed system can help
the information exchanging system over
Image Steganography, LSB, Data hiding,
Significant advancements in digital imaging during
the last decade have added a few innovative
dimensions to the field of image processing [1]. The
word steganography is derived from the Greek words
grafia meaning writing [2].
In image steganography the information is hidden
exclusively in images. It is one of the effective means
of data hiding that protects data from unauthorized or
unwanted disclosure and can be used in various field
such as medicines, research, defense and intelligence
for secret data storage, confidential communication,
protection of data from alteration and disclosure and
access control in digital distribution.
The original files can be referred to as cover text,
cover image, or cover audio. After inserting the secret
message, it is referred to as stego
key is used for hiding/encoding process to restrict
detection or extraction of the embedded data [2]. The
image obtained after insertion of message is called a
stego image. Insertion of secret message is done in
Least Significant Bit (LSB) of the image pixels in this
paper. Then the stego image formed is having a
message which is invisible to human eye. This means
that one cannot find the difference between the
original image and stego image. The secret message is
inserted by using an algorithm and the secret message
is obtained from stego image by using reverse
algorithm [4].
The organization and implementation of the paper is
as follows. The following section p
of Steganography. Then experimental results are
expressed and discussion and conclusion are at the
end.
II. STEGANOGRAPHY
Steganography is a process of secret communication
where a piece of information (a secret message) is
hidden into another piece of innocent looking
information, called a cover. The message is hidden
inside the cover in such a way that the very existence
of the secret information in the cover cannot be
estimated in any suspicion in the minds of the viewers
Research and Development (IJTSRD)
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Dec 2018
Dec 2018 Page: 394
or Information Security System
In image steganography the information is hidden
exclusively in images. It is one of the effective means
of data hiding that protects data from unauthorized or
unwanted disclosure and can be used in various field
cines, research, defense and intelligence
for secret data storage, confidential communication,
protection of data from alteration and disclosure and
access control in digital distribution.
The original files can be referred to as cover text,
or cover audio. After inserting the secret
message, it is referred to as stego-medium. A stego-
key is used for hiding/encoding process to restrict
detection or extraction of the embedded data [2]. The
image obtained after insertion of message is called a
stego image. Insertion of secret message is done in
Least Significant Bit (LSB) of the image pixels in this
paper. Then the stego image formed is having a
message which is invisible to human eye. This means
that one cannot find the difference between the
riginal image and stego image. The secret message is
inserted by using an algorithm and the secret message
is obtained from stego image by using reverse
The organization and implementation of the paper is
as follows. The following section presents the process
of Steganography. Then experimental results are
expressed and discussion and conclusion are at the
STEGANOGRAPHY
Steganography is a process of secret communication
where a piece of information (a secret message) is
ther piece of innocent looking
information, called a cover. The message is hidden
inside the cover in such a way that the very existence
of the secret information in the cover cannot be
estimated in any suspicion in the minds of the viewers

International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
[1]. Steganography is the art and science of
communicating in a way which hides the existence of
the communication. Steganography plays an
important role in information security. It is the art of
invisible communication by concealing information
inside other information.
A digital image is described using a 2-D matrix of the
color intestines at each grid point (i.e. pixel).
Typically gray images use 8 bits, whereas colored
utilizes 24 bits to describe the color model, such as
RGB model. The Steganography system which us
an image as the cover, there are several techniques to
conceal information inside cover image. The spatial
domain techniques manipulate the cover image pixel
bit values to embed the secret information. The secret
bits are written directly to the cover image pixel bytes.
Consequently, the spatial domain techniques are
simple and easy to implement. The Least Significant
Bit (LSB) is one of the main techniques in spatial
domain image Steganography. The LSB is the lowest
significant bit in the byte value of the image pixel.
The LSB based image steganography embeds the
secret in the least significant bits of pixel values of the
cover image (CVR) [7]. One of the common
techniques is based on manipulating Least Significant
Bit (LSB) planes by directly replacing the LSBs of the
cover-image with the message bits. LSB methods
typically achieve high capacity [6]. LSB substitution
is also the process of adjusting the least significant bit
pixels of the carrier image [8].
The concept of LSB Embedding is simple. It exp
the fact that the level of precision in many image
formats is far greater than that perceivable by average
human vision. Therefore, an altered image with slight
variations in its colors will be indistinguishable from
the original by a human being, just by looking at it. In
conventional LSB technique, which requires eight
bytes of pixels to store 1byte of secret data but in
proposed LSB technique, just four bytes of pixels are
sufficient to hold one message byte. Rest of the bits in
the pixels remains the same.
III. LEAST SIGNIFICANT BIT (LSB)
TECHNIQUE
In Least Significant Bit Algorithm, both secret
message and the cover image are firstly converted
from their pixel format to binary. And the Least
Significant Bit of the image is substituted with the bit
of the secret message to be transferred so as to reflect
the message that needs to be hidden. The bits of the
Trend in Scientific Research and Development (IJTSRD) ISSN: 2456
phy is the art and science of
communicating in a way which hides the existence of
the communication. Steganography plays an
important role in information security. It is the art of
invisible communication by concealing information
D matrix of the
color intestines at each grid point (i.e. pixel).
Typically gray images use 8 bits, whereas colored
utilizes 24 bits to describe the color model, such as
RGB model. The Steganography system which uses
an image as the cover, there are several techniques to
conceal information inside cover image. The spatial
domain techniques manipulate the cover image pixel
bit values to embed the secret information. The secret
mage pixel bytes.
Consequently, the spatial domain techniques are
simple and easy to implement. The Least Significant
Bit (LSB) is one of the main techniques in spatial
domain image Steganography. The LSB is the lowest
the image pixel.
The LSB based image steganography embeds the
secret in the least significant bits of pixel values of the
cover image (CVR) [7]. One of the common
techniques is based on manipulating Least Significant
the LSBs of the
image with the message bits. LSB methods
typically achieve high capacity [6]. LSB substitution
is also the process of adjusting the least significant bit
The concept of LSB Embedding is simple. It exploits
the fact that the level of precision in many image
formats is far greater than that perceivable by average
human vision. Therefore, an altered image with slight
variations in its colors will be indistinguishable from
st by looking at it. In
conventional LSB technique, which requires eight
bytes of pixels to store 1byte of secret data but in
proposed LSB technique, just four bytes of pixels are
sufficient to hold one message byte. Rest of the bits in
LEAST SIGNIFICANT BIT (LSB)
In Least Significant Bit Algorithm, both secret
message and the cover image are firstly converted
from their pixel format to binary. And the Least
Significant Bit of the image is substituted with the bit
the secret message to be transferred so as to reflect
the message that needs to be hidden. The bits of the
secret message replace each of the colors of the Least
Significant Bit of the Image [9]. Every pixel in an
image indicates a color and, the each ima
up of pixels [5].
The right-most value is the LSB value of related
image pixel sequence. If the LSB is a 1, then the total
will be an odd number, and if 0, it will be an even
number. However, changing the LSB value from a 0
to a 1 does not have a huge impact on the final result.
Each 8-bit binary sequence is used for expressing the
color of a pixel for an image, so changing the LSB
value from a 0 to 1 does not impose a major change
and it is unlikely to be noticed by an observer. In fact,
the LSBs of each pixel value could be potentially
modified, and the changes would still not be visible.
This provides an enormous amount of redundancy in
the image data, which means that we can effectively
substitute the LSBs of the image data, with each bit of
the message data until the entire message has been
embedded. This is meant by Least Significant Bit
Substitution Method.
One of the earliest stego-systems to surface was those
referred to as Least Significant Bit Substitution
techniques, socalled because of how the message data
is embedded within a cover image c. In computer
science, the term Least Significant Bit (LSB) refers to
the smallest (right-most) bit of
structure of binary is such that each
be either a 0 or a 1, often thought of as off
respectively. Starting from the right, the value (if on)
denotes a 1. The value to its left (if on) denotes a 2,
and so on where the values double each time.
This value essentially determines whether the total
sum is odd or even. If the LSB is a 1, then the total
to a 1 does not have a huge impact on the final f
it will only ever change by +1 at most. If we now
think of each 8-bit binary sequence as a means of
expressing the colour of a pixel for an image, it
should be clear to see that changing the LSB value
from a 0 to a 1 will only change the colour by +
change that is unlikely to be noticed with the naked
eye. In fact, the LSBs of each pixel value could
potentially be modified, and the changes would still
not be visible. This highlights a huge amount of
redundancy in the image data, and means that w
effectively substitute the LSBs of the image data, with
each bit of the message data until the entire message
Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Dec 2018 Page: 395
secret message replace each of the colors of the Least
Significant Bit of the Image [9]. Every pixel in an
image indicates a color and, the each image is made
most value is the LSB value of related
image pixel sequence. If the LSB is a 1, then the total
e a huge impact on the final result.
bit binary sequence is used for expressing the
color of a pixel for an image, so changing the LSB
value from a 0 to 1 does not impose a major change
and it is unlikely to be noticed by an observer. In fact,
SBs of each pixel value could be potentially
modified, and the changes would still not be visible.
This provides an enormous amount of redundancy in
the image data, which means that we can effectively
substitute the LSBs of the image data, with each bit of
the message data until the entire message has been
embedded. This is meant by Least Significant Bit
systems to surface was those
referred to as Least Significant Bit Substitution
because of how the message data
within a cover image c. In computer
Significant Bit (LSB) refers to
most) bit of a binary sequence. The
structure of binary is such that each integer may only
r a 0 or a 1, often thought of as off and on
Starting from the right, the value (if on)
value to its left (if on) denotes a 2,
double each time.
This value essentially determines whether the total
sum is odd or even. If the LSB is a 1, then the total
to a 1 does not have a huge impact on the final figure;
it will only ever change by +1 at most. If we now
bit binary sequence as a means of
expressing the colour of a pixel for an image, it
should be clear to see that changing the LSB value
from a 0 to a 1 will only change the colour by +1 - a
change that is unlikely to be noticed with the naked
eye. In fact, the LSBs of each pixel value could
potentially be modified, and the changes would still
not be visible. This highlights a huge amount of
redundancy in the image data, and means that we can
effectively substitute the LSBs of the image data, with
each bit of the message data until the entire message

has been embedded. This is meant by Least
Significant Bit Substitution. Now if the message is
embedded as is into LSB of the cover image, th
resultant structure in the LSB plane of the stego
image would clearly be a giveaway [3]. The
embedding algorithm at the sender side and extracting
algorithm at the receiver side are presented as follows:
A. The embedding algorithm at the sender side
Step (1) : Get the input cover image and secret
message.
Step (2) : Accept the stego-key from the user and
calculate average value of them.
Step (3) : Convert each character of secret message
and each LSB bit of cover image (R
channel) from the position of
average of stego-key.
Step (4) : Substitute the LSB bit of cover image (R
channel) with binary values of secret
IV. IMPLEMENTATION OF PROPOSED SYSTEM
In this proposed system, the secret message is used to hide in a cover bmp image. Firstly each character of
secret message and each pixel of cover bmp image are converted into binary values. The user has to input
stego-key as the password of stego-key is u
Figure1. Overview of proposed system
After inserting secret message into cover image file,
the resulting stego-image is sent to the receiver
through the desired communication channel. The
above figure 1 shows the overall system design of
proposed system.
While inserting a binary bit of secret message into
cover image, each pixel value of cover image, which
is in decimal in value, is converted into binary values
as shown in figure 2. In this case, there are R, G, B
channel values of each pixel of cover image, but only
has been embedded. This is meant by Least
Significant Bit Substitution. Now if the message is
embedded as is into LSB of the cover image, then the
resultant structure in the LSB plane of the stego-
image would clearly be a giveaway [3]. The
embedding algorithm at the sender side and extracting
algorithm at the receiver side are presented as follows:
The embedding algorithm at the sender side
: Get the input cover image and secret
key from the user and
: Convert each character of secret message
and each LSB bit of cover image (R-
channel) from the position of calculated
: Substitute the LSB bit of cover image (R-
channel) with binary values of secret
message with respect to the starting point
until the end of secret message.
Step (5) : Insert the end character value at the end of
secret message.
Step (6) : Calculate the PSNR, SNR of original and
resulting images.
Step (7) : Send a stego-image to the receiver.
B. The extracting algorithm at the receiver side
Step (1) : Get the input stego
calculate average valu
Step (2) : Load the stego-image that is sent from the
sender.
Step (3) : Extract each of LSB bit from the stego
image until to find out the end bit.
Step (4) : Reconstruct the collecting LSB bits from
the stego-image.
Step (5) : Transform the LSB bits to correspondent
characters.
IMPLEMENTATION OF PROPOSED SYSTEM
key is used to embed the secret message in a cover file.
Figure1. Overview of proposed system
After inserting secret message into cover image file,
image is sent to the receiver
on channel. The
above figure 1 shows the overall system design of
While inserting a binary bit of secret message into
cover image, each pixel value of cover image, which
is in decimal in value, is converted into binary values
figure 2. In this case, there are R, G, B
channel values of each pixel of cover image, but only
the R-channge LSB bit values are used to substitute.
Similarly each character of secret message is
converted from decimal value to binary value. Finally
the converted binary value of secret message is
substituted into each LSB bit of R
image until the end of secret message. In this case the
starting substitution point is chosen according to the
input stego-key. The figure 3 illustrates the
substitution of secret message into each LSB bit of
cover image.
Dec 2018 Page: 396
message with respect to the starting point
until the end of secret message.
: Insert the end character value at the end of
: Calculate the PSNR, SNR of original and
image to the receiver.
The extracting algorithm at the receiver side
: Get the input stego-key from the userand
image that is sent from the
: Extract each of LSB bit from the stego-
image until to find out the end bit.
: Reconstruct the collecting LSB bits from
: Transform the LSB bits to correspondent
sed to embed the secret message in a cover file.
channge LSB bit values are used to substitute.
Similarly each character of secret message is
converted from decimal value to binary value. Finally
verted binary value of secret message is
substituted into each LSB bit of R-channel of cover
image until the end of secret message. In this case the
starting substitution point is chosen according to the
key. The figure 3 illustrates the
tution of secret message into each LSB bit of

Figure2. Pixel representation and binary values
Figure 3.LSB substitution algorithm of proposed
system
While defining the starting point of embedding LSB,
the stego-key is firstly collected from the user. The
summation of the ASCII value of each character of
stego-key is calculated and then the average of those
characters value is computed. While substituting the
secret message into LSB of cover image, the first LSB
position is chosen according to the calculated average
value of input stego-key characters. Then the
substitution processing will continue until the end of
secret message.
V. EXPERIMENTAL RESULTS AND
ANALYSIS
The following figure 4 shows the required processes
at the sender side, in this case, the user has to input
the stego-key which is already shared with the
receiver side. In this research work, the cover image
type of bmp is used to evaluate.
2. Pixel representation and binary values
Figure 3.LSB substitution algorithm of proposed
While defining the starting point of embedding LSB,
from the user. The
summation of the ASCII value of each character of
key is calculated and then the average of those
characters value is computed. While substituting the
secret message into LSB of cover image, the first LSB
ng to the calculated average
key characters. Then the
substitution processing will continue until the end of
EXPERIMENTAL RESULTS AND
The following figure 4 shows the required processes
at the sender side, in this case, the user has to input
key which is already shared with the
receiver side. In this research work, the cover image
Figure 4.Sender side of proposed system
There are three portions at the sender side to accept as
shown in figure 4.The first one is choosing the input
cover image file, and then inputting the desired secret
message and finally stego-key. The stego
important to substitute and to extract secret message
at both sides. The secret message should be arbitrary,
the size of secret message can increase the processing
time of substitution into the cover image.
The following figure 5 shows the overall processes at
the receiver side. The first important one is stego
which is used to evaluate the average value of input
characters. After calculating the average of input
characters, the proposed system can point
starting point to extract the secret message. The
second important one is the sent stego
must be in bmp file format only. Finally the proposed
system can successfully extract the original secret
message with correct stego-key.
Dec 2018 Page: 397
ender side of proposed system
There are three portions at the sender side to accept as
shown in figure 4.The first one is choosing the input
cover image file, and then inputting the desired secret
key. The stego-key is very
tant to substitute and to extract secret message
at both sides. The secret message should be arbitrary,
the size of secret message can increase the processing
time of substitution into the cover image.
The following figure 5 shows the overall processes at
the receiver side. The first important one is stego-key
which is used to evaluate the average value of input
characters. After calculating the average of input
characters, the proposed system can point out the
starting point to extract the secret message. The
second important one is the sent stego-image which
must be in bmp file format only. Finally the proposed
system can successfully extract the original secret
key.

Figure5. Receiver side of proposed system
When the proposed system evaluates, the original
cover image is changed according to the input secret
message and stego-key. The PSNR and SNR values of
original and resulting images are calculated and
compared in the following table 1. The resulting
PSNR and SNR values of substituted cover image
show a little bit changes with the original ones. In this
case, different six bmp cover images are used to
implement the proposed system.
In order to measure the performance of t
compression algorithms two performance parameters
are used in this system.
 Signal to Noise Ratio (SNR)
 Peak Signal to Noise Ratio (PSNR)
A. Signal to Noise Ratio
The signal to noise ratio (SNR) is a technical term
used to characterize the quality of the signal detection
of a measuring system. In this case, a system uses a
Table1. The comparison results of PSNR and SNR values between original and embedded images
No. Types of Images
Original Image
PSNR
1 flower.bmp 22.2474
Receiver side of proposed system
evaluates, the original
cover image is changed according to the input secret
key. The PSNR and SNR values of
original and resulting images are calculated and
lowing table 1. The resulting
PSNR and SNR values of substituted cover image
show a little bit changes with the original ones. In this
case, different six bmp cover images are used to
In order to measure the performance of the image
compression algorithms two performance parameters
The signal to noise ratio (SNR) is a technical term
used to characterize the quality of the signal detection
of a measuring system. In this case, a system uses a
noise “salt and pepper” with the original cover
images.
B. Peak Signal to Noise Ratio (PSNR)
Mean Squared Error (MSE) is defined as the square of
differences in the pixel values between the
corresponding pixels of the two images. The mean
square error (MSE) of N * M size image is given by
the following equation (1),
MSE= ΣM, N [I1 (m, n) –I2 (m, n)]
M &N -number of rows and columns in the input
images.
PSNR (peak signal to noise ratio)
to-noise ratio often abbreviated PSNR, is an
engineering name, for the ratio between the maximum
possible power of a signal and the power of
corrupting noise that affects the fidelity.
The peak error between the compressed image and
original image is measured in terms of PSNR. The
higher value of PSNR indicates higher quality of
image. To calculate PSNR, MSE is first computed.
PSNR value can be derived as in equation (2). Here
‘O’ and ‘D’ are the original and the distorted image
pixel values (binary), respectively, to be compared,
and the image size is M x N.







MSE
MAX
PSNR
2
10log10
Here, MAX is the peak value of the pixels in an image.
MAX is 255 when pixels are presented in an 8
bitformat. Theoretically, the higher the PSNR value
is, the better the image processing is; however,
practically, there are some problems reported in the
literature about the use of the PSNR for image quality
assessment.
1. The comparison results of PSNR and SNR values between original and embedded images
Original Image Embedded Image
Image Size Resulting Images
SNR PSNR SNR
17.5806 22.2568 17.59 255x256
Dec 2018 Page: 398
noise “salt and pepper” with the original cover
Peak Signal to Noise Ratio (PSNR)
an Squared Error (MSE) is defined as the square of
differences in the pixel values between the
corresponding pixels of the two images. The mean
square error (MSE) of N * M size image is given by
I2 (m, n)] 2 / (M*N) (1)
number of rows and columns in the input
PSNR (peak signal to noise ratio) -PSNR Peak signal-
noise ratio often abbreviated PSNR, is an
engineering name, for the ratio between the maximum
possible power of a signal and the power of
corrupting noise that affects the fidelity.
tween the compressed image and
original image is measured in terms of PSNR. The
higher value of PSNR indicates higher quality of
image. To calculate PSNR, MSE is first computed.
PSNR value can be derived as in equation (2). Here
nal and the distorted image
pixel values (binary), respectively, to be compared,
(2)
is the peak value of the pixels in an image.
is 255 when pixels are presented in an 8-
bitformat. Theoretically, the higher the PSNR value
the better the image processing is; however,
there are some problems reported in the
literature about the use of the PSNR for image quality
1. The comparison results of PSNR and SNR values between original and embedded images
Resulting Images

2 mandrill.bmp 21.8341
3 lenna.bmp 21.9866
4 peppers.bmp 21.9163
5 sails.bmp 21.9336
6 boy.bmp 21.9816
VI. DISCUSSION AND CONCLUSION
In this research work, proposed LSB based
steganography for embedding and extracting
algorithms are presented. LSB based steganography
embed the text message in LSB of the pixels of cover
image according to the input stego-key. This paper
also compares the results of PSNR values and SNR
values of original and resulting cover images. The
main goal of this paper is to show how secret image
can be embedded and how it can be sent through the
internet by fooling grabbers.
Many problems can be encountered when important
data is transferred over the public communication
media. A safe and secure procedure is needed to
transfer them easily. For this purpose simple image
hiding techniques are used and the quality of stego
images is also improved by using LSB substitution
algorithms. So the hackers may not estimate secret
message with resultingstego image. The experimental
results show that the stego image and the cover image
remain more or less identical which is the main focus
of this paper. This means that a secret message can be
sent to the destination without changes.
PSNR and SNR values of original and embedded
images are compared and analyzed. The comparison
16.6596 21.8459 16.67 512x512
16.8408 22.0123 16.87 220x220
12.4611 21.9112 12.46 512x512
16.9037 22.0068 16.98 768x512
17.1659 21.9232 17.11 768x512
DISCUSSION AND CONCLUSION
In this research work, proposed LSB based
steganography for embedding and extracting
algorithms are presented. LSB based steganography
embed the text message in LSB of the pixels of cover
key. This paper
also compares the results of PSNR values and SNR
values of original and resulting cover images. The
main goal of this paper is to show how secret image
can be embedded and how it can be sent through the
Many problems can be encountered when important
over the public communication
media. A safe and secure procedure is needed to
transfer them easily. For this purpose simple image
hiding techniques are used and the quality of stego
images is also improved by using LSB substitution
rs may not estimate secret
message with resultingstego image. The experimental
results show that the stego image and the cover image
remain more or less identical which is the main focus
of this paper. This means that a secret message can be
stination without changes. Finally the
PSNR and SNR values of original and embedded
images are compared and analyzed. The comparison
results show that the embedded resulting image is
totally identical with the original ones.
As the further work, other color cover image types
such as jpg, tiff, png and so on will be used to
compare with those results. Another better embedding
and extracting algorithms will be used to implement
it. Also not only secret text message but also secret
image or data will be used to embed in a cover file. It
is expected to find better technique and algorithms to
hide more data in a cover image.
Reference
1. M. Mishra, Department of Information and
Communication Technology and Dr. M.C.
Adhikary, Department of Applied Physics and
Ballistics, “An Easy Yet Effective Method for
Detecting Spatial Domain LSB Steganography”,
International Journal of Computer Science and
Business Informatics, vol.8, No.1 Dec 2013.
2. K. J. Devi, “A Secure Image Steganography using
LSB Technique and Pseudo Ran
Technique”, Department of Computer Science and
Engineering National Institute of Technology
Rourkela Odisha, Bachelor Thesis, May 2013.
Dec 2018 Page: 399
results show that the embedded resulting image is
totally identical with the original ones.
color cover image types
such as jpg, tiff, png and so on will be used to
compare with those results. Another better embedding
and extracting algorithms will be used to implement
it. Also not only secret text message but also secret
ed to embed in a cover file. It
is expected to find better technique and algorithms to
hide more data in a cover image.
M. Mishra, Department of Information and
Communication Technology and Dr. M.C.
Adhikary, Department of Applied Physics and
allistics, “An Easy Yet Effective Method for
Detecting Spatial Domain LSB Steganography”,
International Journal of Computer Science and
Business Informatics, vol.8, No.1 Dec 2013.
K. J. Devi, “A Secure Image Steganography using
LSB Technique and Pseudo Random Encoding
Technique”, Department of Computer Science and
Engineering National Institute of Technology-
Rourkela Odisha, Bachelor Thesis, May 2013.

3. R. Chandramouli, Msync lab, Stevens Institute of
Technology, Dept. of Electrical and Computer
Engineering, Hoboken and N. Memon,
Polytechnic University, Computer Science
Department, Brooklyn, “Analysis of LSB Based
Image Steganography Techniques, IEEE 2001.
4. A. Khurana, Dept of Electronic, Punjab, India and
B. M. Mehta, Dept of ECE, Punjab, India,
“Comparison of LSB and MSB based Image
Steganography, International Journal of Computer
Science and Technology, Vol. 3 Issue 3, July
2012.
5. R. R. Krupa, Department of Information
Technology, the Standard Fireworks, Rajaratnam
College for Women, Sivakasi, Tamilnadu,
“An Overview of Image Hiding Techniquess in
Image Processing”, The SIJ Transaction of
Computer Science Engineering and its
Applications (CSEA), Vol.2, No.2, March
2014.
6. C. Chan, Department of Computer Engineering
and Information Technology, City University of
Hong Kong, Hong Kong, “Hiding Data in Images
R. Chandramouli, Msync lab, Stevens Institute of
Technology, Dept. of Electrical and Computer
Hoboken and N. Memon,
Polytechnic University, Computer Science
Department, Brooklyn, “Analysis of LSB Based
Image Steganography Techniques, IEEE 2001.
A. Khurana, Dept of Electronic, Punjab, India and
B. M. Mehta, Dept of ECE, Punjab, India,
LSB and MSB based Image
Steganography, International Journal of Computer
Science and Technology, Vol. 3 Issue 3, July-Sept
R. R. Krupa, Department of Information
Technology, the Standard Fireworks, Rajaratnam
College for Women, Sivakasi, Tamilnadu, India,
“An Overview of Image Hiding Techniquess in
Image Processing”, The SIJ Transaction of
Computer Science Engineering and its
Applications (CSEA), Vol.2, No.2, March-April
C. Chan, Department of Computer Engineering
ty University of
Hong Kong, Hong Kong, “Hiding Data in Images
by Simple LSB Substitution”, the Journal of the
Pattern Recognition Society, August 2003.
7. B, S. Champakamala, K. Padmini, K. D. Radhika,
Department of TCE, Don Bosco Institute of
Technology, Bangalore, India, “Least Significant
Bit Algorithm for Image Steganography”,
International Journal of Advanced Computer
Technology, Vol.3, No.4, August 2014.
8. C, R, Ravinder, A, R, Roja, Department of Master
of Computer Appliccations, Teegala Krishna
Reddy Engineering College, Medbowli, Meerpet,
Hyderabad, “The Process of Encoding and
Decoding of Image Steganography using LSB
Algorithm”, International Journal of Computer
Science and Engineering Technology”, Vol.2,
Issue 11, Nov 2012.
9. 9. O. Osunade, and I. A. Ga
Computer Science, University of Ibadan, Ibadan,
“Enhancing the Least Significant Bit (LSB)
Algorithm for Steganography”, International
Journal of Computer Application, Vol. 149, No.3,
Sept 2016.
Dec 2018 Page: 400
by Simple LSB Substitution”, the Journal of the
Pattern Recognition Society, August 2003.
B, S. Champakamala, K. Padmini, K. D. Radhika,
Department of TCE, Don Bosco Institute of
galore, India, “Least Significant
Bit Algorithm for Image Steganography”,
International Journal of Advanced Computer
Technology, Vol.3, No.4, August 2014.
C, R, Ravinder, A, R, Roja, Department of Master
of Computer Appliccations, Teegala Krishna
ineering College, Medbowli, Meerpet,
Hyderabad, “The Process of Encoding and
Decoding of Image Steganography using LSB
Algorithm”, International Journal of Computer
Science and Engineering Technology”, Vol.2,
O. Osunade, and I. A. Ganiyu, Department of
Computer Science, University of Ibadan, Ibadan,
“Enhancing the Least Significant Bit (LSB)
Algorithm for Steganography”, International
Journal of Computer Application, Vol. 149, No.3,

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