Comparative Study of Spatial Domain Image Steganography Techniques

Int. J. Advanced Networking and Applications
Volume: 07 Issue: 02 Pages: 2650-2657 (2015) ISSN: 0975-0290
2650
Comparative Study of Spatial Domain Image
Steganography Techniques
Rejani. R
Department of Computer Science and Engineering, Manonmanium Sundarnar University, Tirunelveli
Email: rejani@gmail.com
Dr. D. Murugan
Department of Computer Science and Engineering, Manonmanium Sundarnar University, Tirunelveli
Email: dhanushkodim@yahoo.com
Deepu.V.Krishnan
Technical Architect, Infosys Limited, Technopark, Karyavattom Campus.
Email: deepu_vk@hotmail.com
----------------------------------------------------------------------ABSTRACT----------------------------------------------------------
Steganography is an important area of research in information security. It is the technique of disclosing
information into the cover image via. text, video, and image without causing statistically significant modification
to the cover image. Secure communication of data through internet has become a main issue due to several passive
and active attacks. The purpose of stegnography is to hide the existence of the message so that it becomes difficult
for attacker to detect it. Different steganography techniques are implemented to hide the information effectively
also researchers contributed various algorithms in each technique to improve the technique’s efficiency. In this
paper we do a brief analysis of different spatial domain image stegnography techniques and their comparison.
The modern secure image steganography presents a challenging task of transferring the embedded information to
the destination without being detected.
Keywords – Cover image, Embedding, LSB, MSB, RGB, Steganography, Stego-image.
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Date of Submission: July 20, 2015 Date of Acceptance: Aug 12, 2015
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1. INTRODUCTION
Today information technology has developed to a great
extent which was not imaginable in earlier years. These
days almost all the methods of communication has become
digital and for the exchanging of information we are
mainly dependent on the internet. Through different
locations across the globe we can exchange a variety of
information. However there is also a possibility that these
information may be sometimes illegally gathered,
transferred and used by some malicious users or
organizations for their use. This results in confidential and
private data being used by another person without consent
which could lead to dangerous consequences. These issues
features that data protection has become an important
point to consider during data communication. The main
challenge faced in data privacy is the need to share data
while protecting personally identifiable information from
hackers and other malicious attacks. We have seen that
steganography and cryptography together can protect data
effectively.
Different steganography techniques have been
developed to hide the message in an image. Steganography
method hides the textual information in such a way that
only sender and receiver can identify that a message is
hidden within the image. Steganography on images can be
broadly classified as spatial Domain Steganography and
Frequency Domain Steganography. This paper does a
comparison between some of the spatial Domain
steganography techniques.
1.1 LSB Steganography
The simplest and most popular image Steganography
method is the least significant bit (LSB) substitution. In
this method the messages are embedded into cover image
by replacing the least significant bits of the image directly.
The hiding capacity can be increased by using up to 4 least
significant bits in each pixel which is also quite hard to
detect [16, 18, 3].
1.2 MSB Steganography
This method is a slight modification of the LSB
steganography. In this method instead of changing the
least significant bit the most significant bit is changed. In
this case the embedded value is stored in the most
significant bits of the image.
1.3 RGB Steganography
A Digital image is an array of numbers that represent light
intensities at various points or pixels. Digital computer
images can be normally stored as 24-bit (RGB) or 8-bit
(Grayscale) files. A 24-bit file can be quite large however
it provides more space for hiding information. As we
know all colors are essentially a combination of three
primary colors: red, green, and blue. Every primary color
is represented by one byte ie every pixel represents a
combination of (R,G,B).

2651
Paper [11] is based on the manipulation of the least
significant bits of pixel values [3][4] or the rearrangement
of colors to create least significant bit or also called as
parity bit patterns. The parity bit patterns can correspond
to the message being hidden. RGB Steganography method
attempts to overcome the problem of the sequential
fashion and the use of stego-key for the selection of pixels.
In paper [3] some least significant bit based RGB
steganography method is implemented.
1.4 Pixel Value Differencing Steganography
This method is different when compared to the LSB and
RGB based steganography methods. The Pixel-value
differencing steganography technique uses the difference
value between two consecutive pixels in a block to
determine how many bits of text could be embedded Wu
and Tasi's method has the two quantization range tables.
As per that the first table using the range of widths (8, 8,
16, 32, 64 & 128) to provide large capacity and the second
table uses the range widths of (2, 2, 4, 4, 4, 8, 8, 16, 16,
32, 32 & 64) which meanwhile provides a high
imperceptibility [23].
2. RELATED WORK
Let us see some of the related work done in each of these
area. In [3] one type of lsb based RGB intensity
steganography technique proposes an improved LSB (least
significant bit) based Steganography technique for images.
This paper deals with an embedding algorithm for hiding
encrypted messages in nonadjacent and random pixel
locations in edges and smooth areas of images. At first it
encrypts the secret message, then detects edges in the
cover-image by using improved edge detection filter. After
that the message bits are embedded in the least significant
byte of randomly selected edge area pixels and 1st 3rd and
4th LSBs of red, green, blue combinations respectively
across randomly selected pixels across smooth area of
image. Some other types of LSB steganography
techniques based on least bits are explained on [18]. And
some similar technique is implemented in [16] MSB based
steganography.
In RGB Intensity Based Variable-Bits Image
Steganography [4] describes new algorithm for RGB
image based steganography. This algorithm introduces the
concept of storing variable number of bits in each channel
(R, G or B) of pixel based on the actual color values of
that pixel: lower color component stores higher number of
bits. Secure RGB Image Steganography method from
Pixel Indicator to Triple Algorithm-An Incremental
Growth [1] introduces two methods of RGB image
steganography one is pixel indicator technique and other is
triple-A algorithm. They uses the same principle of LSB,
As the secret is hidden in the least significant bits of the
pixels, but also offer a better randomization while
selecting of the number of bits and color used. This
randomization increase the security of the system and it
also increase the capacity. Some other types of RGB based
steganography techniques are implemented in [1, 2, 11,
and 14]. These techniques can be applied to RGB images
where each pixel is represented by three bytes to
indicate the additive values of red, green, and blue.
Paper [5] designs a steganography algorithm which not
only hide the message behind the image but also provide
more security than others. For the purpose of security,
encryption technique is used with a user-defined key. In
that paper, message is hide into an image in the form of an
image that is using image generation method message is
converted into the image of predefined format and then by
using designed algorithm that image will hide into the
cover image. RGB image format is used to improve the
quality of the stego image. Finally this RGB image is
saved as BMP image file so that no lossy compression can
occur and the original message do not destroy and can be
extract as it is.
In the Pixel Indicator High Capacity Technique for
RGB Image Based Steganography [6] give the ideas from
the random pixel manipulation methods and the stego key
ones techniques are merged, which uses the least two
significant bits of one of the channels to indicate
existence of data in the other two channels.
In [24] the method of Wang et al. uses Pixel-Value
Differencing (PVD) and their modulus function provides
high capacity and good image quality. In this method the
embedding process creates a number of artifacts like
abnormal increases and fluctuations in the PVD histogram,
thereby revealing the existence of the hidden message. To
enhance the security of the algorithm it makes use of a
turnover policy that prevents abnormal increases in the
histogram values and a novel adjusting process is devised
to remove the fluctuations at the border of the subrange in
the PVD histogram. This method therefore eliminates all
the weaknesses of the PVD steganography methods thus
far proposed and guarantees a secure communication.
Paper [25] the PVD approach is where the cover image is
first partitioned into small squares. After this each square
is rotated by a random degree of 0, 90, 180 or 270. The
resulting image is again divided into non-overlapping
embedding units with three consecutive pixels, and the
mid one is used for data embedding. The number of
embedded bits in this method is dependent on the
differences between the three pixels. In order to preserve
the local statistical features, the three pixel values will
have the same sort order after data hiding. Also the new
method can first use sharper edge regions for data hiding
adaptively, thereby helping to preserve other smoother
regions by adjusting a parameter.
3.VARIOUS STEGANOGRAPHY
TECHNIQUES
We have by now seen the various methods used to hide
data using steganography. The usual and most popular
among these methods is the LSB or the Least Significant
Bit Steganography. Different modifications of LSB

2652
techniques are familiar like LSB1 Steganography and
LSB2 steganography. Likewise different variable
algorithms are implemented for RGB and PVD
steganography. Here we are investigates some of the
algorithms of these techniques.
3.1 LSB Steganography
This is the simplest of the steganography methods based in
the use of least significant bit and therefore is also the
most exposed. In this technique the embedding process is
quite simple and consists of the sequential substitution
of each Least Significant Bit (LSB-1) of the image
pixel for the bit message [18]. Even though this method
is simple, It can disclose a large volume of data. The LSB1
algorithm procedures describes below:
Step 1: The data bits are changed from decimal to
binary.
Step 2: The Cover image is read.
Step 3: The cover image is converted from decimal to
binary format.
Step 4: The text to be disclosed inside the image and is
broken into bits.
Step 5: Take the first 8 bytes of data from the cover
Image. (This step will be looped)
Step 6: Embed the data bit to be hide in the least
significant bit of the cover image one by one.
Let us took the first byte of original data from the Cover
image be:
E.g.:- 1 1 0 1 1 0 0 0
First bit of the data to be hidden: 1
Replace the least significant bit
1 1 0 1 1 0 0 0
1 1 0 1 1 0 0 1
This process will be continued for first 8 byte of data
and conceal the first byte of data.
step 7: Continue the step 6 for all pixels in the file.
Image after the disclosing process of data using the
LSB-1 Steganography.
Fig. 1 Cover image Fig. 2 Stego image
3.2 MSB Steganography
This is another common and simple approach to
embedding information in an image. In this method the
most significant bit (or the 1st bit) of some or all of the bits
inside an image is changed to a bit of the message to be
disclosed. Consider an example - a grid of 3 pixels of a 24-
bit image can be follows:
(00101101 00011100 01011110)
(10100110 11100100 00001100)
(11011010 10101101 01101011)
When the number 200, whose binary representation is
11001000, is kept in the least significant bit of this part of
the image the grid will become as follows:
(00101101 00011100 01011110)
(10100110 11100100 00001100)
(11011010 10101101 01101011)
The number was embedded into the first 8 bits of the
grid however only the 5 underlining bits need to be
changed according to the disclosed message. Usually half
of the bits in an image will need to be modified to hide a
secret message using the maximum cover size on an
average. As a consequence there are 256 possibilities of
varying intensities of each primary color, altering the most
significant bit of a pixel results in only a small change in
the intensity of the color. These small changes cannot be
perceived by the human eye and thus the message is
successfully hidden.
Fig.4 Cover image Fig.5 Stego image
3.3 RGB Steganography
RGB based algorithms are another method of
steganography which can be used for hiding text into
images. In this method [11] it uses to change the least
significant bits of pixel values (3) or sometimes (4) of the
rearrangement of colors to create parity bit patterns or
least significant bit which correspond to the message being
hidden. Along with this variable bit steganography
technique is also used in RGB based steganography.

2653
Image after embedding the data using modulus RGB
steganography technique.
Fig. 5 Cover image Fig. 6 Stego image
3.4 Pixel Value Differencing Steganography
In this method capacity of each pixel is determined by
using the PVD technique. Then password is combined
with 5th bit of that pixel to hide the properties of data. On
the assumption that the password matches the data is
inserted directly and otherwise inverted data is inserted
into the pixel. Therefore at some places the data is inserted
inverted and at other places it is hidden directly into the
image. In case a hacker who does not have the knowledge
of secret key tries to extracts the data it is of no use.
Another inspection is that size of embedded bit is
unknown without key. So with the fault key, the message
cannot be resumed. The data disclosing steps are as
following:
1) Open the cover image, read the pixels and create the
RGB matrixes for color images and gray matrix for gray
scale images.
2) Insert the secret key after converting it into bit stream.
3) Determine gray level variation near target pixel and
thus determine capacity of that pixel say „n‟.
4) Employ the XOR function on the 5th bit of cover image
pixel with one bit taken from the secret key.
5) If password matches, “n‟ bits of message data are
embedded in cover pixel.
6) Otherwise data is inverted before embedding.
7) Repeat the steps from 3 to 6 until the whole data is
stored into the image.
8) After this the Size of message file is embedded into last
100 bytes using same procedure.
9) Then the data is disclosed into the cover image along
with the normal image is saved to get the stego image.
4. EVALUATION CRITERIA OF
TECHNOLOGIES
Human Vision cannot identify the small differences in the
pictures when comparing a normal image and a stego
image which has the embedded text. However there are
various evaluation techniques which can be used to find
out the differences between original picture and the stego
image. When we are inserting message into the picture the
image data will be changed slightly. In most cases it will
not be noticeable to human eyes but it will affect the size,
image quality, and picture information. Correspondingly
with the size of content and technique used for
steganography the image’s noise may be increased. Here
we are discussing about some of the available techniques
used to find out the changes of digital images.
4.1 PSNR (Peak-Signal to Noise Ratio)
PSNR value of the encoded image (Peak Signal to Noise
Ratio) is normally calculated to find the noise of the
image.
MSE (Mean Squared Error) is the first value to be
calculate using the formula:
The PSNR is defined as:
MAX1 = Maximum possible pixel value of the image.
The minimum value of MSE denotes that both the
given images are having almost same kind. Higher the
PSNR value denotes the reconstruction is of higher
quality. For all type of color images with RGB values per
pixel the definition of PSNR is the same except the MSE
is the sum over all squared value differences divided by
image size and by three. Meanwhile, for color images the
image is converted to a different color space and PSNR is
reported against each channel of that color space.
The embedding capacity of the image can be tested
using the PSNR ratio.
4.2 Root Mean Square Error
The root-mean-square deviation (RMSD) or root-mean-
square error (RMSE) is a frequently used measure of the
differences between values predicted by a model or an
estimator and the values which are actually observed. The
root-mean-square deviation will provide us a view of the
sample standard deviation or the differences between the
predicted values and the observed value. The differences
are also called as residuals when the calculations are
performed over the data samples that are used for
estimation. They are also called prediction errors when
computed out-of-sample. The RMSD value helps to
collate the magnitudes of the errors in calculations for
various times into a single measure of predictive power.

2654
As of these factors the RMSD is generally considered as a
good measure of accuracy.
In some disciplines, the RMSD is used to compare
differences between two things that may change, no more
which is accepted as the "standard". When measuring the
average of the difference between two time series ( )
and ( ) this formula becomes:
4.3 Mean Squared Error
The mean squared error (MSE) of an estimator which
represents the average of the squares of the "errors", i.e is,
the difference in between the estimator and what is
estimated. Mean square error is similar to the risk
function, which is equivalent to the expected value of the
squared error loss or also called as quadratic loss. This
distinction comes because of randomness or because the
estimator doesn't account for information that could
produce a more accurate estimate.
If vector of n predictions and
is the vector of examined values corresponding
to the inputs to the function which generated the
predictions,
This is a known value which is a calculated value given
for a particular sample (and hence is sample-dependent).
The MSE of estimator with respect to the unknown
parameter is defined as
The MSE is calculated as the sum of the variance and
the squared bias of the estimator or of the predictions. For
MSE of an estimator,
4.4 Structural Similarity Index
The structural similarity (SSIM) index is another method
for measuring the similarity between two images. SSIM
method is a full reference metric because the measuring of
image quality is actually based on an initial uncompressed
or distortion-free image as a reference. This calculation is
designed to improvement over the traditional methods like
peak signal-to-noise ratio (PSNR) and mean squared error
(MSE), which are proven to be inconsistent with human
eye perception.
The difference with respect to other techniques
mentioned previously such as MSE or PSNR is that these
approaches estimate perceived errors. After all SSIM the
image degradation is considered as a perceived deviation
in the structural information. Structural information means
that the pixels have strong inter-dependencies especially
when they are spatially close. These dependencies has
important information about the structure of the objects in
the visual scene.
The SSIM metric is calculated on various windows or
sections of an image. To compute between two positions x
and y of common size N×N is:
With μx the average of x;
μy the average of y;
σ
2
x the variance of x;
σ
2
y the variance of y;
σxy the covariance of x and y;
c1=(k1L)
2
, c2=(k2L)
2
two variables to stabilize the
division with weak denominator;
L the dynamic range of the pixel-values (this is
2
#bits per pixel
−1);
k1=0.01 and k2=0.03 by default.
5. TEST RESULTS
Till now we have discussed about the various
Steganography methods as well as the different evaluation
criteria to test these methods. Here we are evaluating the
above mentioned steganography techniques and analyzing
the techniques.
Test1:
The sample image used for testing is image of penguins
below. The size of this image is 1.5 MB and the format of
the image is .bmp. As part of our tests a standard text
message of 445 byte was inserted into the image and then
the PSNR, MSE and SSIM value was calculated.

2655
The value of the test images are show below:
Size of the Cover
image
1.5 MB
Format BMP
Number of Pixels 1614600
Below table depicts the results for each of the
steganography methods after embedding the message into
the cover image by comparing the original image and the
stego image.
LSB MSB RGB PVD
PSNR 93.07 92.9 94.2 92.1
MSE 0.00003 0.00003 0.00003 0.00029
SSIM 1.00 1.00 1.0 1.0
Once the message has been embedded in cover image
the change in the pixel value will not be noticed to human
eyes in the stego image. It is quite hard to find the very
minor color changes in the cover and stego image by
human eye. To identify this a histogram comparison is
used by steganalyst to identify the stego image by
comparing the histogram of cover image and stego image.
Based on these tests that have been conducted separate
histograms are drawn for cover and stego image.
Histogram – Cover image (Penguin)
Histogram of Penguin image after embedding message
Mean:2014 Median:223 Standard deviation:56.2
Test2:
The sample image used for test2 is image of squirrel
below.
The size of this image is 2.5 MB and format is BMP. As
part of the tests a standard text message of 500 byte was
inserted into the image and then the PSNR, MSE and
SSIM value was calculated.
Size of the Cover image 2.5 MB
Format BMP
Number of pixels 2399296
stego image.
LSB MSB RGB PVD
PSNR 92.77 92.5 94.0 92.4
MSE 0.00004 0.00003 0.00003 0.00022
SSIM 1.00 1.00 1.0 1.0
Histogram – Cover image (squirrel)
Histogram of squirrel after embedding text
Mean: 100.2 Median:87 Standard deviation:48.5

2656
Test3:
The sample image used for testing is image of Tajmahal
below. The size of this image is 5.5 MB and format is
BMP. As part of the tests a standard text message of 1000
byte was inserted into the image and then the PSNR, MSE
and SSIM value was calculated.
Mean: 96.3 Median: 100 Standard deviation: 67.2
Size of the Cover image 5.5 MB
Format BMP
Number of pixels 5760000
stego image.
LSB MSB RGB PVD
PSNR 90.11 89.9 92.24 90.02
MSE 0.00005 0.00004 0.00004 0.00033
SSIM 1.00 1.00 1.0 1.0
Histogram – Original image (Taj Mahal)
Histogram of Taj Mahal After embedding text
6. CONCLUSION
Steganography method is used to hide data into images
without evoking suspicion to hackers as well as other
attackers. There is different classification of
steganography based on the algorithm used internally. In
this paper we have compared LSB, MSB, RGB and PVD
methods of steganography and also tested each one of
them. The tests were done to identify which one of these
methods is better from the other in terms of noise
produced and changes made to the pixels. From our tests
we could find out that the RGB based steganography
method out performs the other methods in terms of PSNR
and MSE calculation while SSIM calculation remained the
same for all methods. It is also more difficult to identify
by hackers when compared with LSB and MSB methods.
Hence it is more advantageous over the other two methods
for use of steganography.
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