Journal of Intelligent Learning Systems and Applications, 2012, 4, 199-206
doi:10.4236/jilsa.2012.43020 Published Online August 2012 ( 1
Reversible Digital Image Watermarking Scheme Using Bit
Replacement and Majority Algorithm Technique
Koushik Pal1, Goutam Ghosh1, Mahua Bhattacharya2*
1Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India; 2Indian Institute of Information Technology and
Management, Gwalior, India.
Email: *
Received September 24th, 2011; revised May 17th, 2012; accepted May 25th, 2012
The current paper presents a new digital watermarking method through bit replacement technology, which stores multi-
ple copies of the same data that is to be hidden in a scrambled form in the cover image. In this paper an indigenous ap-
proach is described for recovering the data from the damaged copies of the data under attack by applying a majority
algorithm to find the closest twin of the embedded information. A new type of non-oblivious detection method is also
proposed. The improvement in performance is supported through experimental results which show much enhancement
in the visual and statistical invisibility o f hidden data.
Keywords: Additive Noise; Salt and Pepper Noise; Compression; Filtering; Averaging; Multiple Watermarking;
Majority Algorithm
1. Introduction
Recent history has witnessed the rapid development in
information technologies that has given an extended and
easy access to digital information. Along with several
developments it leads to the problem of illegal copying
and redistribution of digital media. As a result, the integ-
rit y and confidentiality of the digital information has come
under immense threat. Digital watermarking, an e mergi ng
technology, came in order to solve the problems [1].
Digital watermarking is a technique which allows an
individual to add hidden copyright notices or other veri-
fication messages or even classified information into
digital media [2,3].
Watermarks can either be visible or invisible. Here in
this paper we utilize the invisible watermarking tech-
nique. This is used in public in formation settings such as
digital image libraries, museums, and art galleries and
also in defense communication where data security is of
prime importance [4]. Watermark embedding utilizes
two kinds of methods, one in the spatial domain and the
other in th e transfor m domain. In th e spatial doma in the
watermark is directly embedded into the image pixels
whereas in the frequency domain the image is decom-
posed into blocks and then mapped into the transform
domain [5].
Watermarking is basically a process of hiding infor-
mation in an image known as cover image. According to
the behavior it is of two types: robust and fragile. Copy-
right protection is achieved by robust watermarking [6]
while image authentication is usually achieved b y fragile
watermarking techniques [7-9]. Robust watermarking
technique ensures the quality of the hidden information
by several protection algorithms. In the fragile water-
marking scheme if any alter ation of the message is found
then the message breaks up by itself and can be easily
detected as tampered by the provider of the watermark
[1]. Invertible watermarking is a new process which en-
ables the exact recovery of the original image upon ex-
traction of the embedded info rmation.
In the present paper the work implements both authen-
tication and confidentiality in a reversible [10] manner
without affecting the image in any way. Security of im-
ages imposes three mandatory characteristics: confiden-
tiality, reliability and availability [11]. Confidentiality
means that only the entitled persons have access to the
images. Reliability has two aspects: 1) Integrity—means
the image has not been modified by a non-authorized
person [12]; 2) Authentication corroborates that the im-
age belongs indeed to the correct person and is issued
from an authorized source. Availability is the capacity
that an image is available to the entitled persons in the
normal conditions of access and exercise [13].
1.1. LSB Watermarking and Its Limitation
The most straight-forward method of watermark embed-
ding would be to embed the watermark into the least-
*Corresponding a uthor.
Copyright © 2012 SciRes. JILSA
Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique
significant-bits (LSB) of the cover object [14]. Given the
extraordinarily high channel capacity of using the entire
cover for transmission in this method, a smaller object
may be embedded multiple times [8]. Even if most of
these are lost due to attacks, a single surviving water-
mark would be considered as a success. LSB substitution,
however, despite its simplicity brings a lot of drawbacks.
Although it may survive from all these transformations
such as cropping, addition of noise or lossy compression
etc. A better attack would be to simply set the LSB bits
of each pixel to defeat the watermark with negligible
impact on the cover object. Furthermore, once the algo-
rithm is discovered, the embedded watermark could be
easily modified by an intermediate party. An improve-
ment on basic LSB substitution would be to use a pseu-
do-random number generator to determine the pixels to
be used for embedding based on a given “seed” or key
[1]. The algorithm however would still be vulnerable to
replacing the LSB’s with a constant. Even locations that
were not used for the watermarking bits, the impact of
the substitution on the cover image would be negligible.
LSB modification proves to be a simple and fairly power-
ful tool for steganography [15], however, it lacks the
basic robustness that watermarking applications require.
1.2. Attack and Distortion
In practice, a watermarked image may be altered either
on purpose or accidentally. The watermarking system
should be robust enough to detect and extract the water-
mark [16]. Different types of alterations or attacks can be
done to degrade the image quality by adding distortions.
The distortions are limited to those factors which do
not produce excessive degradations; otherwise the trans-
formed object would be unusable. These distortions also
introduce degradation on the performance of the water-
mark extraction algorithm [17,18]. Methods or a combi-
nation of methods, considered unintentional are used
intentionally as an attack on a watermarked image in
order to render the watermark undetectable.
Compression is a common attack, as data transferred
via network is often compressed using JPEG. High qual-
ity images are often converted to JPEG to reduce their
size. Another method is deletion or shuffling of blocks.
In images rows or columns of pixels may be deleted or
shuffled without a noticeable degradation in image quality.
These may render an existing watermark undetectable.
Salt and pepper noise is another type of attack that re-
places the intensity levels of some of the pixels of an
image resulting in loss of information from those pixels.
Some of the best known attacks are mentioned here; they
may be intentional or unintentional, depending on the
In this paper we have taken two very popular attacks
known as Salt and pepper noise and image compression.
2. Proposed Watermarking Technique for
Data Authentication
Our proposed methodology for data hiding does not
follow the conventional LSB technique because of the
inherent limitations mentioned earlier. A new digital water-
marking scheme described here, uses several bits of the
cover image starting from the lower order to the higher
order to hide the information logo. Several sets of the
same data forming the information logo are hidden into
the cover image. Thus, even if some of the information is
lost due to attack, we can still collect the remaining
information from the cover image and reconstruct the
hidden information resembling the original one [19].
The detail algorithm for both the embedding scheme
and the recovery scheme is given bellow.
2.1. Embedding the Digital Watermark
Step 1: Two images are taken as input: First of all, the
cover image and the message or information logo are
taken as inputs. The cover image is taken to be a gray
scale image. The logo or information is a binary image,
basically a sequence of 0’s and 1’s.
Step 2: The Size of the images is extracted: Next to
make the program compatible to run for any size of the
cover image and information logo keeping in mind the
data carrying capacity of the cover image the dimensions
of the respective images are extracted.
Step 3: Normalizing and reshaping the logo: After
normalizing the information logo it is reshaped in one
Step 4: Transforming the cover image into wavelet
domain using dwt: The cover image is transformed to
the wavelet domain u sing discrete wavelet tran sform. W e
use Haar transform to do the DWT. Here the 1st level
DWT was used to obtain more capacity for hiding the
information. The cover image is decomposed into 4 sub-
domains as HH, HL, LH and LL according to the different
frequencies of t he cover image.
[HHi,HLi,LHi,LLi] = dwt2(cover_image,'haar');
Step 5: Calculating the length of the transformed
cover image and 1d l og o:
Step 6: Calculating the size of each sub domain de-
composed cover image and reshaping them in to 1d:
Step 7: Det ermining th e maximum co efficient value
Copyright © 2012 SciRes. JILSA
Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique 201
of each of the 4 sub dom ains
Step 8: Finding the position to hide the information
logo into the transformed logo: The position for hiding
the binary logo in each sub domain must be in between
zero and t he maxim um coefficient val ue of t hat sub dom ain.
for i = 1: 1 : cover_HL_size_m*cover_HL_size_n
if(HL1(i)<(max_ HL1) && HL1(i)>(0))
pos_ HL1(c)=i;
Step 9: Hiding a number of sets of the same infor-
mation logo in hl and lh domain: More than one set of
the same information is being hidden in HL and LH band
or domain for easier and good quality recovery. The hid-
ing process in each of these domains follows a specific
formula. The formula is that the black dots in each set of
1d information logo is hidden in a position of informa-
tion logo from where a constant value is subtracted.
for i = 1: 1 : length_of_HL
if logo_res(i)==0
Step10: Reshaping the decomposed image back to
its normal dimension:
Step11: Writing the watermarked image to a file
and displaying it.
2.2. Recovery of the Embedded Watermark
We have assumed that the cover image hiding the wa-
termark is available at the receiving end. So again, in the
process of recovery we first take the original image used
to hide the information. Along with it we also send the
receiver of the message, 3 keys which essentially act as
private keys. These keys are required to decrypt and to
the extract the encrypted or embedded messages.
Step 1: The watermarked and original images are
taken as inputs.
Step2: Finding the 1st level decomposition of both
the inputs using DWT:
[HHi,HLi,LHi,LLi] = dwt2(orig_image,'haar');
Step 3: Finding the size of each sub domain of both
the decomposed input images:
Step 4: Reshaping each of the decomposition of
both watermarked and original cover images into 1D:
Step 5: Taking the two input keys equal to the di-
mension of the logo to find the size o f the 4 decompo-
sitions of the logo:
key_M=input('Enter the no of rows KEY 1 :');
key_N=input('Enter the no of cols KEY 2:');
Step 6: Determining the maximum coefficient val-
ues of the original cover image:
Step 7: Finding the positions used to hide the logo
for each decom posit ion:
for i = 1: 1 : orig_HL_size_m*orig_cA_size_n
if(HLo(i)<(max_HLo) && HLo(i)>(0))
Step 8: Extracting the positional sets for different
sets of the logo from each decomposition:
for i = 1 : 1 : len_set_HLo
Step 9: Recovery of the different sets of logo from
each of the sub bands and construction of the final
logo from the different recovered sets using majority
for i = 1: 1 : key_M*key_N
Step 10: After reshaping, displaying each of the re-
covered sets of logo and the final constructed logo:
if rec_wmark_V_set_3(i)==1
Copyright © 2012 SciRes. JILSA
Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique
2.3. Design Flow of the Proposed Scheme
Embedding Technique:
Gray scale co v er image (CI) and binary
logo (information ) taken as input
Extracting the image size and
reshaping both images to 1D form
Determine min intensity of CI (threshold)
and zero intensity positions o f logo
Pixel positions of CI within threshold
range arranged in an array
Encryption:( Random key)
(Zero intensity position of Logo)
The positional values in the array are used to
hide the encrypted information
Decoding Technique:
Extractio n of position of hiding using
Threshold value
Extraction of the encrypted
positional information
Filtering by Majority Algorithm on the 8
retrieved sets to get the derived data set
Decryption of the rec ove r ed data
using first key
Reconstruction of the message logo
with the receiver keys
Watermarked image
Recovered f i n a l information
logo/ watermark
3. Image Quality Metrics
To measure the amount of visual quality degradation be-
tween origina l and watermarked images, diff erent types o f
image quality metrics are used. In the present work we
have used peak signal-to-noise ratio (PSNR) and struc-
tural similarity index measure (SSIM).
3.1. Peak Signal-to-Noise Ratio (PSNR)
It is the ratio between the maximum possible power of a
signal and the power of corrupting noise that affects the
fidelity of its representation. PSNR is usually expressed
in terms of dB for a wide range of signals The PSNR is
most commonly used as a measure of quality of recon-
struction for lossy compression. The cover image in this
case is the original data, and the information logo is the
error introduced by watermarking. When comparing the
deformed image with the original one an approximation
to human perception of reconstruction quality is made.
Therefore in some cases one reconstruction may appear
to be closer to the original than another, even though it
has a lower PSNR. So a higher PSNR would normally
indicate that the reconstruction is of higher quality.
It is most easily defined via the mean square error
(MSE) which for two m × n monochrome images I and K,
where one of the images is considered a noisy approxi-
mation of the other, is defined as:
 
11 2
SEI i jK i j
The PSNR is defined as:
here, MAX1 is the maximum possible pixel value of the
image. When the pixels are represented using 8 bits per
sample, this is 255.
3.2. Structural Similarity Index Measure (SSIM)
It is a method for measuring the similarity between two
images. The SSIM index is a full reference metric, where
the measure of the image quality is based on an initial
distortion-free image as reference. SSIM is designed to
improve on traditional methods like PSNR and MSE,
which have proved to be inconsistent with human eye
perception. The resultant SSIM index is a decimal value
between –1 and 1. The value 1 is only reachable in the
case of two identical sets of data. The SSIM metric is
calculated on various w indows of an image. The measure
between two windows x and y of common size N × N is:
Copyright © 2012 SciRes. JILSA
Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique
Copyright © 2012 SciRes. JILSA
22 22
,xy xy
xy xy
SSIMx ycc
 
 
 In Section 4.4, eight different sets of the recovered
logo and the final constructed logo using majority algo-
rithm are given.
where μx is the average of x; μy the average of y; 2
variance of x; 2
the variance of y; σxy the covariance
of x and y; c1 = (k1L)2, c2 = (k2L)2 are two variables to
stabilize the division with weak denominator; L the dy-
namic range of the pixel-values ( typically this is 2# bits per
pixel – 1); k1 = 0.01 and k2 = 0.03 by default.
4.1. Embedding of Watermark into Cover Image
and Quality Metrics
It can be observed from Table 1 that the results obtained
from the quality metrics are very satisfactory and hence
we can conclude fr om the obtained dat a t hat t h e wat ermarked
image is not very much different from the original cover
image that is being used. Also, the difference between
the watermarked image and the original appears almost
the same to the human eye.
4. Results and Discussions
In this section several experimental results are given to
show the outcome of the proposed watermarking tech-
nique. Higher PSNR value indicates good quality of picture.
After embedding the information logo in the cover image
for Lena, Tower and Fruit images we find that the PSNR
value is quite high .
In Section 4.1 three sets of cover image along with
three information logos are taken as input. The water-
marked image is shown after embedding. The computed
value of the quality metrics are also given to find the
image quality.
Similarly SSIM is another measuring metric used for
finding the similarity between the two images. Here we
observe that after embedding the information logo the
similarity between the cover image and watermarked
image is 0.98 which describes a good structural similar-
ity between these two images.
In Section 4.2, the watermarked images and the recov-
ered information logos are given.
In Section 4.3, the outcome for the same recovery
technique is shown but under two attacks known as salt
and pepper noise and image compression. For salt and
pepper noise the percentage is varied up to 40% and
compression up to 5%. The required noisy watermarked
images and the recovered logo from those images are
4.2. Recovery of Watermark from Watermarked
Image without Any Attack
Here the hidden watermarked image i.e. the information
logo is successfully recovered from the un-attacked
Table 1. Watermark embedding and image quality metrics.
Cover Image (dim-256 × 256) Message Image (dim-16 × 16)Watermarked Image (dim-256 × 256)PSNR in dB SSIM
42.343 0.9889
Lena S logo Watermarked Lena
41.806 0.9781
Tower K logo Watermarked Tower
41.506 0.9853
Fruit Max Payne logo Watermarked Fruit
Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique
watermarked image which is given in Table 2. Primarily,
we have considered the communication is ideal and hence
no external interference or attack has been included.
In practice, however, we have to consider noise, which
is dealt with in the next section.
4.3. Recovery of Watermark from Watermarked
Image under Attacks
In watermarking terminology, an attack is an y processing
that may impair detection of the watermark or commu-
nication of the information conveyed by the watermark.
The processed watermarked data is then called attacked
data. There are two kinds of watermark attacks: Non-
intentional attacks, such as compression of a legally ob-
tained, watermarked image or video file, and intentional
attacks, such as an attempt by a multimedia pirate to de-
stroy the embedded information and prevent tracing of
illegal copies of watermarked digital video.
The present work describes the following two types of
(1) Salt and pepper noise.
(2) JPEG Compression.
(1) Salt and pepper noise:
In this section we have demonstrated the proposed
watermarking technique after using the salt and pepper
noise to corrupt the watermarked images up to 40%.
From the above set of results in Table 3, it is clear that
that the proposed algorithm can withstand 40% salt and
Table 2. Recovered watermark or hidden message from
un-attacked watermarked image.
Watermarked Image
(dimension 256 × 256) Recovered Message Image
(dimension 16 × 16)
Lena S logo
Tower K logo
Fruit Max Payne logo
Table 3. Recovered watermark or hidden message from salt
and pepper noise attacked watermarked image.
(dimension 256 × 256)
( Salt and Pepper Noise)
Message Image
(dimension 16 × 16)
Lena 10% S logo
Lena 20% S logo
Lena 30% S logo
Lena 40% S logo
pepper attack with ease and the information logo that is
derived from the watermarked image closely resembles
the information logo that was embedded into the image.
Hence we can say that the proposed algorithm efficiently
handles salt and pepper noise.
Similarly in the next Table 4, the strength of the pro-
posed algorithm is demonstrated against the salt and
pepper attack with a different set of data.
(2) JPEG Compression:
The performance of the proposed algorithm is also
demonstrated against JPEG compression attack in Table
5. This algorithm also demonstrates its strength against
compression attack as well.
4.4. Construction of Different Recovered Logo
Using Majority Algorithm Technique
Tables 6 and 7 describe the outcome of majority algo-
rithm technique. Here final constructed S logo and K
logo from 8 different recovered sets have been shown. It
is clearly visible that the proposed majority algorithm
technique is strong enough to construct the information
logo from some distorted sets of recovered logo.
Copyright © 2012 SciRes. JILSA
Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique 205
Table 4. Recovered watermark from salt and pepper noise
attacked watermarked image.
Image Attacked Image
(Salt and Pepper Noise) Recovered
Message Image
Fruit 10% Max Payne
Fruit 20% Max Payne
Fruit 30% Max Payne
Fruit 40% Max Payne
Table 5. Recovered watermark from JPEG compressed
watermarked image.
Image Attacked Image (JPEG
Compression) Recovered
Message Image
Fruit Quality = 99% Max Payne
Tower Quality = 98% K logo
Lena Quality = 95% S logo
Table 6. Derived S logo from 8 sets of recovered noisy logo
using majority algorithm.
S logo
from 1st set
S logo
from 2nd set
S logo
from 3rd set
S logo
from 4th set
Recovered S
logo from 5th
Recovered S
logo from 6th
Recovered S
logo from 7th
Recovered S
logo from 8th
S logo
from these
8 set using
Table 7. Derived K logo from 8 sets of recovered noisy logo
using majority algorithm.
K logo
from 1st set
K logo
from 2nd set
K logo
from 3rd set
K logo
from 4th set
K logo
from 5th set
K logo
from 6th set
K logo
from 7th set
K logo
from 8th set
K logo
from these
8 set using
SSIM is used for finding the similarity between the two
images. Table 8 describes the quality of the recovered
logo. The similarity between the original logo and the
recovered logo from the watermarked image is measured
using SSIM. The following results describe that the pro-
posed algorithm is quite efficient for salt and pepper
noise up to 40% and JPEG compression resulting in im-
age quality distortion = 95%.
5. Conclusions
In present paper the proposed algorithm for digital
watermarking aims at obtaining a solution to the several
problems of digital communication and also for data
hiding. It is seen that the proposed algorithm is robust
against compression and “salt and pepper” noise attacks
where a private key is required for the recovery of the
hidden information and which enhances security to the
algorithm. Since digital watermarking has many app-
lications in the digital world toda y it can be thought of as
a digital communication scheme where an au xiliary mes-
sage is embedded in digital multimedia signals and is
available wherever the latter signals move.
The results obtained show satisfactory statistics of the
performance of the proposed algorithm. The obtained
PSNR and SSIM values support the quality of the en-
cryption method. It is also seen that the embedded inf orma-
Copyright © 2012 SciRes. JILSA
Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique
Copyright © 2012 SciRes. JILSA
Table 8. SSIM values for different sets of recovered water-
mark or information logo under salt and pepper noise and
JPEG compression attack.
Used Logo
(dimension 16×16) Types of AttackAmount of
Distortion SSIM
20% 0.9554
30% 0.9032
S Logo Salt and
Pepper Noise
40% 0.7954
Quality = 99% 0.9687
Quality = 98% 0.8654 S Logo JPEG
Quality = 95% 0.7496
mation is successfully recovered from the watermarked
image by using the majority algorithm technique. We can
conclude by stating that the proposed algorithm provides
a method for secure data hiding.
6. Acknowledgements
It is my pleasure to express my gratitude to all of the
faculty members of Institute of Radio physics and Elec-
tronics, University of Calcutta, Kolkata.
I am very much thankful to all the of the facu lty mem-
bers of Electronics and communication Department, prin-
cipal and the authority of Guru Nanak Institute of Tech-
nology, S o d e po r e , K o lk a t a f o r t h e i r un grudgi ng support.
I am also very grateful to my fami ly memb er s fo r t he ir
continuous encouragement.
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