The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), Vol. 2, No. 2, March-April 2014
An Overview of Image Hiding
Techniques in Image Processing
R. Rathna Krupa*
*Department of Information Technology, The Standard Fireworks Rajaratnam College for Women, Sivakasi, Tamilnadu, INDIA.
E-Mail: rathnakrupa89@gmail.com
Abstract—Image hiding techniques embed a secret image into another image. The fusion of the secret image
and the cover image is the resultant stego image. Using the image hiding techniques, existence of the secret
image in the final image will be unaware by an unintended observer, so that it can be relocated carefully. One
of the common techniques is the LSB (Least Significant Bit) substitution method. It includes the manipulation
of the least significant bits in the original image. To enhance the quality of the final image the Optimal Pixel
Adjustment Process - OPAP which disarranges the image bits is used. In this paper, different types of LSB
schemes and image enhancing schemes are focused.
Keywords—Embedding Techniques; Image Embedding; Image Hiding; Least Significant Bits Substitution;
Optimal Pixel Adjustment Process.
Abbreviations—Least Significant Bit (LSB); Mean Square Error (MSE); Moderately Significant bit (MSB);
Optimal Pixel Adjustment Process (OPAP).
I.
I
INTRODUCTION
NTERNET has become more popular and common nowa-days. Sending the important data through the net has
become a serious threat. Moreover routing the data in an
encrypted format is very common and embedding the secret
message in an image is a challenge. To safeguard the images
from the illegal hindrance or interception, techniques such as
watermarking, image hiding is used for the safe transmission
of images. This paper focuses on such techniques and ideas.
A watermark is a signal or a pattern that is embedded into an
image. It is usually done for content authentication.
Watermarking methods are divided into: fragile
watermarking methods and robust watermarking methods. In
robust watermarking methods, the secret information will
always be robust against any changes or malfunctions. They
are usually used for protecting copyrights. On the other hand,
fragile watermarking methods are usually designed so that
they can easily get broken. Therefore it is applicable for
tamper detection and authentication. Chandramouli & Nasir
Memon (2001) has used the watermarking technique with
public key verification for watermark insertion and extraction
procedure. This can be used for fingerprinting, ownership
assertion and for ID-card security purpose. Image
authentication against the quantization attack has been
proposed by Hongtao Lu et al., (2003).
In watermarking, the size of the symbol should be
usually small, ranging from one bit to thousands of bits to
represent the symbol. The goal of watermarking focuses on
the existence of the symbol which is usually small after
undergoing certain manipulations and by degrading
ISSN: 2321-2381
operations such as filtering, resampling, lossy compression,
etc. But in Image Hiding, it is mainly used to transmit
important and confidential images, whose size tends to be
very large.
Many image hiding methods have been proposed by
Thien & Lin (2003), Chan & Cheng (2004), Li Zhi & Sui Ai
Fen (2004), Wang & Tsai (2007), Wang & Moulin (2007)
and many more. Most of these methods does not consider
about the compressing of cover image. But Chung et al.,
(2001) proposed the usage of singular value decomposition
and vector quantization for compressing the cover image. The
goal of the image hiding technique is to hide the secret image
in such a way that it is invisible to the viewers.The Least
Significant Bit substitution method and Optimal Pixel
Adjustment process are the two image hiding techniques in
which the LSB method is used for embedding the secret
image and the OPAP method is used for enhancing the final
image so that the presence of the secret image is not visible.
Wang et al., (2001) proposed a method called the Moderately
Significant Bit (MSB) where the fourth bit is taken into
account for image hiding. This paper deals with all the facts
used for the procedure of hiding the image and making the
secret message invisible to the grabbers.
This paper is organized as follows. The section II gives
the details of how the LSB method works for hiding an image
and the other algorithms that is suggested for efficient image
hiding mechanism. The section III describes the Optical Pixel
Adjustment Process which is used to improve the quality of
the resultant image, so that the embedded image is unlikely to
be seen by naked eyes. The section IV describes the statistical
analysis process to measure the quality of the final image.
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The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), Vol. 2, No. 2, March-April 2014
The section V explains about the experimental results. In the
final section the conclusion of this survey and future work
about image hiding is reported. The objective of this study is
to know about the image hiding techniques and means to
enhance the quality of the image after an image is embedded
into it so that protection of the data is maintained.
II.
LEAST SIGNIFICANT BIT SUBSTITUTION
METHOD
Every pixel in an image indicates a color and the each image
is made up of pixels. The lower values of the pixel in a gray scale image signifies dark areas and the higher values signify
light areas. So in order to adjust the shades of the image its
values can be adjusted and 8 bits are required to represent
these values [Lin-Yu Tseng et al., 2008].
For example consider the following 8-bit binary
sequence: 10110011. Summing up all these values where 1
exists, will yield a result of 179. The right-most value is the
LSB of this sequence. This value essentially determines
whether the total sum is odd or even. 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.
The procedures used in the LSB method are:
 Get the input cover image and message image.
 Resize the message image same as that of cover
image.
 Shift the message image pixel values over four bits
to right.
 Make last four bits (LSBs) in the pixel values of the
cover image to zero.
 Finally add the images obtained from step 3 and 4 to
get final image.
There are other several LSB techniques used for image
hiding. They are:
 LSB array based technique using encryption by RSA
algorithm in which four least significant bits are
explored by Gandharba Swain & Saroj Kumar
Lenka (2012).
 Transforming LSB Substitution for Image-based
Steganography in Matching Algorithms [ChengHsing Yang & Shiuh-Jeng Wang, 2010].
 LSB substitution based on image blocks and
maximum entropy [Mohamed Radouane et al.,
2013].
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Dumitrescu et al., (2003) used a technique that is based
on a finite state machine whose states are selected multisets
of sample pairs, called trace multisets. Some of the trace
multisets are equal in their expected cardinalities, if the
sample pairs are drawn from a digitized continuous signal.
Random LSB flipping causes transitions between these trace
multisets with given probabilities, consequently alters the
statistical relations between the cardinalities of trace
multisets.
The method proposed by Puech (2009) combines
compression, data hiding and partial encryption of images in
a single processing step. This approach can embed data into
the image according to the message size and partially encrypt
the image and the message without changing the original
image content.
LSB based techniques pose a difficult challenge to a
stego-analyst as it is difficult to differentiate final image and
the cover image which is given as input. The differences
between them will be very slight. Major advantage of this
method is, it is quick and easy. This method works well with
gray scale images. It is mainly used for image integrity. The
quality of the embedding result cannot be degraded so
significantly that the grabbers may notice the existence of the
important data [Ran-Zan Wang et al., 2001].
Figure 1: Cover Image
Figure 1 is the cover image in which the message image
is going to be hidden. It is a grey scale image and this is the
image which will be seen clearly by the viewer.
Figure 2: Secret Image/Message Image
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The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), Vol. 2, No. 2, March-April 2014
Figure 2 is the secret image that is going to be embedded
in the cover image. This image is also a grey scale image in
which the each pixel is shifted right for undergoing the LSB
method.
2.3. Pixel Value Differencing
In this method the cover image is divided into blocks that
consist of two consecutive pixels and the difference between
them is also calculated. If the difference is large then it means
they are present at the edged areas otherwise they are present
at smooth areas. The pixel in the edged area can have larger
amount of pixel values. So, more data can be hidden in the
edged areas [Rahul Samant & Shrikant Agrawal, 2011].
Before embedding, the difference value is then replaced by
the new value. This method is more efficient than the LSB
replacement scheme and it is more easy and feasible too.
III.
Figure 3: Result of LSB Method
After applying the LSB method the resultant image is the
one that is given in Figure 3.In Figure 3 the secret image is
slightly visible when it is seen keenly. This can be tackled by
using some image enhancing algorithms such as Optimal
Pixel Adjustment Process.
2.1. Optimal LSB
When LSB method is used for data hiding, the resultant
image which is the stego image has only small differences
when compared with the cover image, but the differences can
be easily detected using statistical analysis. So Optimal LSB
insertion which is an enhanced method is used for getting
more accurate stego image. In this method an adjustment
process is used to find the correct matching pixel. The closest
pixel value when compared with the secret image to that of
cover image is selected and then embedding is done in that
optimal pixel. This method is more efficient, but finding the
closest pixel requires an additional phase. This method first
starts by initialising three pixel values and comparison takes
place with the cover image pixel which is used to improve the
quality and decrease the error in the stego image [Morteza
Bashardoost et al., 2013].
OPTIMAL PIXEL ADJUSTMENT PROCESS
An optimal pixel adjustment process is proposed to enhance
the quality of the final image obtained by the simple LSB
substitution method. Moreover, the Optimal Pixel Adjustment
Process only requires a checking of the embedding error
between the original cover image and the final image
obtained by the LSB method to form the final enhanced final
image.
In-order to improve the quality different techniques has
been followed. Zhang and Wang introduced Exploiting
Modification Direction (EMD). Furthermore Lee et al.,
(2007) proposed an improved scheme where it uses two
pixels at a time, and gives both pixels a fixed evaluation
value. Both these techniques get compromised if encryption
techniques become public. So Wen-Chung Kuo (2013) used
two kinds of weight-changing evaluation EMD data hiding
strategy. One of them uses the table checking and the other
uses EMD data hiding.
According to the Optimal Pixel Adjustment Process if
the error lies between the particular range, then pixels of the
image that is retrieved after the LSB method is manipulated
accordingly and then the enhanced image pixel is processed.
For example if the error lies between 2 k-1 to 2k and if the pixel
of the image that is produced from the LSB method is greater
than 2k then the pixel present in the final enhanced image is
processed with the pixel from the LSB method otherwise the
enhanced image pixel will be similar to the input image pixel.
In the same way if the error lies between -2k-1 to 2k-1, the pixel
in the enhanced image will not be modified [Chi-Kwong
Chan & Cheng, 2001].
2.2. LSB Matching Scheme
The LSB Matching scheme also modifies the least significant
bits of the pixel for hiding the image but if any one of the bit
in the secret image does not match the LSB of the cover
image, then random addition or subtraction of the bits from
the cover image pixel takes place [Ker, 2004]. In this process
if the number of bits in the message image is lesser than the
cover image, then pseudo-random permutation is used to deal
with it. In the LSB replacement scheme the LSB’s of the
cover image is overwritten by the secret bit stream and the
other procedures are similar to the LSB Matching Scheme. It
has an advantage of being computationally less complex
when compared with other schemes.
Figure 4: Result of Optimal Pixel Adjustment Process
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The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), Vol. 2, No. 2, March-April 2014
Figure 4 is the final image where the image that is
retrieved after undergoing the LSB method is used in the
OPAP process as the input image.
This Optimal Pixel Adjustment Process improves the
quality of the final image, by reducing the Mean Square Error
between the cover image and the final image. So the hackers
will not be aware of the secret image hidden in it. The
Computational complexities are also reduced considerably.
By doing simple adjustment in the pixel values, the enhanced
image can easily obtained. Once the image is sent over the
internet, the receiver receives it and uses a simple image
retrieval algorithm to retrieve the message in it. There will be
some degradation of message quality due to loss of least
significant bits. So the contrast will be decreased slightly but
this will be negligible.
Message
Image
Cover Image
Least Significant Bit
Substitution Method
Stego Image
Wei Liu et al., (2010) proposed a resolution progressive
compression scheme which compresses an encrypted image
progressively in resolution, such that the decoder can observe
a low-resolution version of the image, study local statistics
based on it, and use the statistics to decode the next resolution
level.
IV.
Then statistical analysis is done in order to study the quality
of the images that is obtained from LSB substitution and
OPAP process. The Mean Square Error (MSE) and the Peak
Signal to Noise Ratio (PSNR) are the two error metrics used
to compare image compression quality. Mean Square Error
(MSB) is used to estimate the quality difference between the
original cover image and the final image. The value of MSE
should be as low as possible so that the quality of the
resultant image is same as the cover image that was given as
input. The value of the MSE will be equal to zero for
identical Images.
Peak signal-to-noise ratio (PSNR) represents a measure
of the peak error. The higher the PSNR value, the better the
quality of the final image. The equation for PSNR is,
(255 )2
PSNR = 10log
𝑑𝐵.
MSE
The MSE value is necessary to calculate the value of
PSNR [Lin-Yu Tseng et al., 2008].
V.
Optimal Pixel
Adjustment Process
Enhanced
Image
Figure 5: Overall Process of Image Hiding
Figure 5 represents the complete process of the image
hiding mechanism in which the enhanced image with
minimum Mean Square Error is achieved. The enhanced
image which is the final image will have the embedded image
hidden in it and it would be unseen by any observer or
viewer. To find the difference between the cover image and
final image statistical analysis method should be performed.
3.1. Recovery Process
In order to recover the secret image at the receiver’s side the
pixel of the image that is shifted left at the LSB process
should be shifted right. The procedures are:
 Get the retrieved image as input.
 Shift the message image pixel values over four bits
to the left.
 Message image is obtained.
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STATISTICAL ANALYSIS
EXPERIMENTAL RESULTS
This section presents experimental results obtained from two
images. The first image is the cover image and the second
one is the secret image. Whenever, according to the
procedure done in Section III and Section IV which is the
LSB and OPAP process, the Mean Square Error for the stego
image is 42.06. The error will be even more higher if the
Optical Pixel Adjustment Process is not made and that will be
the reason for the slight visibility of the secret image in the
cover image. When genetic algorithm is imposed in the final
stego image the error will be decreased and the quality will
also be enhanced considerably.
Therefore, a good image hiding method should be easy
to use and at the same time, the presence of the secret image
should not be viewable. i.e. the value of Mean Square Error
should be lesser and the PSNR value should be greater to get
a proper enhanced result.
VI.
CONCLUSION
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 are encountered when
transferring important data over the network. 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 different
mechanisms. So the hackers may not the stego image and will
© 2014 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), Vol. 2, No. 2, March-April 2014
know nothing about the embedded secret image in it. 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 any glitch and this can be used for
image integrity protection and in places where important,
undisclosed secrets should be sent to the recipient. Research
is going on in privacy protection and intellectual property
rights protection.
In the future, it is expected to find an even better
technique and procedure to hide more data in a cover image.
The future focus should be made on even more less
modification in the cover image and the differences between
the cover image and the stego image should be null when
statistically analyzed.
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R. Rathna Krupa received the degree in
Master of Science for computer science and
Information Technology under Madurai
Kamarajar University, Madurai. She has
worked in open source projects at a private
company in Rajapalayam, Virudhunagar.
She has some special interests on research
fields like image processing, data hiding and
embedding.
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