Journal of Information Security, 2011, 2, 169-184
doi:10.4236/jis.2011.24017 Published Online October 2011 (http://www.SciRP.org/journal/jis)
Copyright © 2011 SciRes. JIS
169
On Secure Digital Image Watermarking Techniques
Manjit Thapa1, Sandeep Kumar Sood2*
1Department of Computer Science, Sri Sai College of Engineering & Technonogy, Badhani, Pa thankot, India
2Department of Computer Science and Engineering, G.N.D.U.R.C., Gurdaspur, Punjab, India
E-mail: manjit.thapa@ya hoo.co.in, *san1198@gmail.com
Received July 29, 2011; revised September 9, 2011; accepted September 20, 2011
Abstract
Digital watermarking is used to hide the information inside a signal, which can not be easily extracted by the
third party. Its widely used application is copyright protection of digital information. It is different from the
encryption in the sense that it allows the user to access, view and interpret the signal but protect the owner-
ship of the content. One of the current research areas is to protect digital watermark inside the information so
that ownership of the information cannot be claimed by third party. With a lot of information available on
various search engines, to protect the ownership of information is a crucial area of research. In latest years,
several digital watermarking techniques are presented based on discrete cosine transform (DCT), discrete
wavelets transform (DWT) and discrete fourier transforms (DFT). In this paper, we propose an algorithm for
digital image watermarking technique based on singular value decomposition; both of the L and U compo-
nents are explored for watermarking algorithm. This technique refers to the watermark embedding algorithm
and watermark extracting algorithm. The experimental results prove that the quality of the watermarked im-
age is excellent and there is strong resistant against many geometrical attacks.
Keywords: Digital Image Watermarking, Singular Value Decomposition, Watermark Embedding Algorithm,
Watermark Extracting Algorithm, Ratio Analysis, Security Analysis
1. Introduction
Digital watermarking is a technique that embeds data
called watermark into a multimedia object so that wa-
termark can be detected to make an assertion about the
objects. It can be categorized as visible or invisible. Ex-
ample of visible watermarking is the logo visible super-
imposed on the corner of television channel in a televi-
sion picture. On the other hand, invisible watermark is
hidden in the object, which can be detected by an au-
thorized person. Such watermarks are used for suit the
author authentication and detecting unauthorized copying.
The novel technology of digital watermarking has been
sponsored by many consultants as the best method for
such multimedia copyright protection problem [1,2].
Digital watermarking is having a variety of useful appli-
cations such as digital cameras, medical imaging, image
databases, video on demand systems, and many others.
In recent years, many digital image watermarking tech-
niques have been proposed in the literature which is
based on spatial domain technique and frequency domain
technique. These techniques are used in watermark em-
bedding algorithm and watermark extracting algorithm.
In 2002, Ali [3] proposed an approach based on DWT
and DCT to improve the performance of the DWT-based
watermarking algorithms. In this method, watermarking
is done by embedding the watermark in first and second
level of DWT sub-bands of the host image, followed by
the application of DCT on the selected DWT sub-bands.
The combination of these two transforms improved the
watermarking performance considerably in comparison
with only watermarking approaches. They showed that
the quality of watermark image is very good. In 2005,
Chen [4] proposed a singular value decomposition
scheme based on components of D and U without using
DWT, DCT and DFT transforms. They showed that
quality of watermarked image is good on their schemes.
In 2007, Seed [5] introduced a novel digital watermark-
ing method based on single key image for extracting dif-
ferent watermarks. In this method, they used Arnold
transform technique in watermark embedding and ex-
traction, which is based on DWT and DCT algorithm.
With the popularity of internet and availability of large
storage devices, storing and transferring an image is
M. THAPA ET AL.
170
i
simple and feasible. They showed that robustness of the
algorithm against many signal processing operations. In
2010, Lamma and Ali [6] suggested two blind, imper-
ceptible and robust video watermarking algorithms that
are based on singular value decomposition. Each algo-
rithm integrates the watermark in the transform domain.
They used the components of matrices such as U and V.
Their schemes are shown to provide very good perform-
ance in watermarked video as compared to Chan [4].
Most of the domain transformation watermarking tech-
niques works with DCT and DWT. However singular
value decomposition (SVD) is one of the most powerful
numeric analysis techniques and used in various re-
quirements. These requirements can be organized and
described as follows [7-10].
Undeletable: An embedded watermark is difficult to
detect and cannot be removed by an illegal person. Also
the algorithm must resist different attacks.
Perceptually visible: The original images and water-
marked images cannot be distinguished by the human
eye. This means that there is not enough alteration of a
watermarked image to prevent motivation to an illegal
person.
Unambiguous: An embedded watermark selected
from a watermarked image that must be clear enough for
ownership to be determined. In this way, the extracted
watermark cannot be distorted to such an extent that the
original watermark cannot be recognized.
In this paper, we will describe a digital image water-
marking algorithm based on singular value decomposi-
tion technique. This paper is organized as follows. In
Section 2, we introduce the SVD transformation and
SVD based watermarking techniques briefly. In Section
3, we propose the embedding and extracting algorithm.
In Section 4, we evaluate the performance of watermark
image. In section 5, we show the experimental results
and Section 6 conclude the paper.
2. A Review of Related Work
Singular value decomposition (SVD) is a mathematical
technique based on linear algebra and used by factoriza-
tion of a real matrix or complex matrix, with many useful
applications in signal processing and statistics.
2.1. Singular Value Decomposition (SVD)
Singular value decomposition is one of a number of
valuable numerical analysis tools which is used to ana-
lyze matrices. It can be appeared from three jointly
compatible points of view. On the other hand, we can see
it as a method for transforming correlated variables into a
set of uncorrelated ones that better expose the various
relationships among the original data items. At the same
time, SVD is a method for identifying and ordering the
dimensions along which data points demonstrate the
most variation. This attach the third way of viewing sin-
gular value decomposition, which accepted the most
variation, it’s possible to find the best approximation of
the original data points using less dimensions. Hence,
SVD can be seen as a method for data reduction. In SVD
transformation, a matrix can be decayed into three ma-
trices that are having the same size as the original matrix.
It is useful to establish a contrast with Gaussian elimina-
tion and its equation. Given A is a n × n square matrix,
this matrix can be decomposed into three components, L,
D and U, respectively such that
[L D U] = SVD (A), A’ = LDUT, L–1 where A = LDU.
1,1 1,21,1,11,21,
2,1 2,22,2,12,22,
3,1 3,23,3,13,23,
1,1 1,21,
T
2,1 2,22,
1
3,1 3,23,
=
nn
nn
nn
nn
nii
i
n
ll l
lll
lll
uuu
uuu lu
uuu



 
 
 
 
 





(1)
where the L and U components are real unitary matrices
or complex matrices with small singular values, and the
D component is an n × n diagonal matrix with larger
singular value or eigen vector values entries which spec-
ify σ1,1 >> σ2,2 >> σk,k,k+1 = σn,n = 0. are non zero
matrix by diagonals of A. SVD is nonlinear because the
orthogonal matrices L and U depend on A and shown in
Equation (1). Ais the reconstructed matrix after the in-
verse SVD transformation. Reduced singular value de-
composition is the mathematical technique underlying a
type of document retrieval and word semblance method.
These are also known as Latent Semantic Indexing or
Latent Semantic Analysis. In this way, the three compo-
nents of matrices L, D, and U specify Aui = σili and µiTA
= σiuiT.
SVD Example
A matrix is said to be square if it has the same number
of rows as columns. To designate the size of a square
matrix with n rows and columns, it is called n-square
matrix. For example, the matrix below is 3-square. As an
example to simplify SVD transformation, suppose
A =
1021 15
30 923
185329





If SVD operation is useful on this matrix, then the ma-
trix A will be decomposed into equivalent three matrices
as follows:
Copyright © 2011 SciRes. JIS
M. THAPA ET AL. 171
L = ,
D = ,
U =
0.4019 0.12020.9079
0.47490.8749 0.9417
0.7830 0.46900.4083


68.5399 00
024.2485 0
00 0.5342




0.44920.7233 0.5242
0.6995 0.64980.2972
0.5557 0.2332 0.7979







Here diagonal elements of matrix D are singular val-
ues and we observe that these values satisfy the non in-
creasing order: 68.5399 24.2485 0.5342.
Digital image watermarking techniques has several
advantages that used singular value decomposition.
Firstly, SVD transformation from the size of memory is
not fixed and can be represented by a rectangle or square
matrices. Secondly, SVD increase accuracy and decrease
the memory requirement. Thirdly, digital images in sin-
gular values are less affected if general image watermark
is executed. Fourth, singular value decomposition in-
clude by algebraic properties.
2.2. SVD-Transformation
We will describe a digital image watermarking technique
which is based on singular value decomposition trans-
form, such as DWT and DCT. Watermarking is estab-
lished by the wavelet coefficient of selected sub bands
and followed by the requirements of DCT transform on
the selected sub-bands [11-14]. In this section, we will
introduce the transformation of digital watermarking
technique.
The DCT Transform: The discrete cosine transform
is a transformation technique based on digital water-
marking algorithm and spatial domain technique. The
discrete cosine transform is derived from discrete Fourier
transforms and represents data in terms of frequency
space rather than an amplitude space. The spatial domain
technique can be transformed into the frequency domain,
and the frequency domain technique can be transformed
back to the spatial domain by using inverse discrete co-
sine transform. The discrete cosine transform (DCT) is a
technique for converting a signal into effortless fre-
quency components. It represents an image as a sum of
sinusoids of varying magnitudes and frequencies. With
an input image, k and the DCT coefficients for the trans-
formed output image, L is computed according to Equa-
tion (2) as shown below. In the equation, k, is the input
image having N × M pixels, k(m, n) is the intensity of the
pixel in row m and column n of the image and L(u, v) is
the DCT coefficient in row u and column v of the DCT
matrix. The DCT formulas are as follows.
The general equation for a one dimension (N data
items) DCT is defined by the following equation:

M1
0
2cos(21)π
L(
M2M
x
mu
um k
)m
(2)
and the corresponding inverse 1D DCT transform is sim-
ple L–1(u)
where
10
=2
11,2, otherwi
u
m
u
se
The m
function computes the two-dimensional
discrete cosine transform (DCT) of an image. The DCT
has the property that, for a typical image, most of the
visually significant information about the image is con-
centrated in just a few coefficients of the DCT. For this
reason, the DCT is often used in image compression ap-
plications. The general equation for a 2D (N by M image)
DCT is defined by the following Equation (3):

M1N1
00
22
L, MN
cos(2 1)πcos(2 1)π
(,)
2M 2N
xy
uvm n
mu nu
kmn





(3)
and the corresponding inverse 2D DCT transform is sim-
ple L–1(u, v), i.e.:
where
10
=2
11,2,,M
u
m
u

1
10
=2
11,2,,N
v
n
v

1
The image is reconstructed by applying inverse DCT
operation according to Equation (4):

M1N1
00
22
K(,)MN
cos(2 1)πcos(2 1)π
,
22
uv
mnm n
mu nu
luv
MN





(4)
Examples for Transformation: The grayscale Lena
image of 256 × 256 pixels, with 4-bit representation for
each pixel is used as the test input. The input image is
divided into 4-by-4 or 8-by-8 blocks, and the two-di-
mensional DCT is computed for each block. The DCT
coefficients are then quantized, coded, and transmitted.
Copyright © 2011 SciRes. JIS
M. THAPA ET AL.
Copyright © 2011 SciRes. JIS
172
changing component of the signal. In most cases, high
pass component is not so rich with data offering good
property for compression. In some cases, such as audio
or video signal, it is possible to contend with some of the
samples of the high pass component without noticing any
significant changes in signal. Filters from the filter bank
are called wavelets and as shown in Figure 2.
The JPEG receiver (or JPEG file reader) decodes the
quantized DCT coefficients, computes the inverse two-
dimensional DCT of each block, and then puts the blocks
back together into a single image. For typical images,
many of the DCT coefficients have values close to zero;
these coefficients can be discarded without seriously
affecting the quality of the reconstructed image as shown
in Figure 1. The transform matrix computation method
is used. The test image was compressed to different
scales, from one to three and the compression ratio as
well as the mean square root error of the reconstructed
image were calculated for minimal error case and quan-
tised case.
For 2-D images, applying DWT corresponds to proc-
essing the image by 2-D filters in each dimension. The
filters divide the input image into four non-overlapping
multi-resolution sub-bands LL1, LH1, HL1 and HH1. The
sub-band LL1 represents the coarse-scale DWT coeffi-
cients while the sub-bands LH1, HL1 and HH1 represent
the fine-scale of DWT coefficients. To obtain the next
coarser scale of wavelet coefficients, the sub-band LL1 is
further processed until some final scale N is reached.
When N is reached we will have 3N + 1 sub-bands con-
sisting of the multi-resolution sub-bands LLy and LHy,
HLy and HHy where y ranges from 1 until N. It has the
following steps in digital image watermarking transfor-
mation such as
The popular block-based DCT transform segments is
an image non-overlapping blocks and applies DCT to
each block. This result in giving three frequency coeffi-
cient sets: low frequency sub-band, mid frequency sub-
band, and high frequency sub band. The digital water-
marking based on two facts. The first fact is that much of
the signal energy lies at low- frequency sub-band which
includes the most important visual parts of the image.
The second fact is that high frequency components of the
image are generally detached through compression and
noise attacks. The watermark is surrounded by modify-
ing the coefficients of the middle frequency sub-band so
that the visibility of the image will not be overstated and
the watermark will not be removed by compression.
Step 1: Present DWT on the original image to de-
compose it into four non-overlapping multi-resolution
coefficient sets, such as LL1, HL1, LH1 and HH1.
Step 2: Present DWT again on two HL1 and LH1 sub-
bands to get eight smaller sub-bands and prefer four co-
efficient sets: HL12, LH12, HL22 and LH22 as shown in
Figure 1.
Discrete Wavelet Transform: The transformation pro-
duct is a set of coefficients organized in the way that
enables not only spectrum analyses of the signal, but also
spectral behavior of the signal in time. This is achieved
by decomposing signal, breaking it into two components,
each concerned information about source signal. Filters
from the filter bank used for decomposition come in
pairs: low pass and high pass. Low pass filtered signal
contains information about slow changing component of
the signal, looking very similar to the original signal,
only two times shorter in term of number of samples.
High pass filtered signal be full of information about fast
Figure 1. Reconstructed image, compression image and quan-
tization image by using DCT.
Figure 2. Sketch map of DWT and DCT decomposed sub-bands.
173
M. THAPA ET AL.
Step 3: Present DWT again on four sub-bands, such as
HL12, LH12, HL22 and LH22 to get sixteen smaller coeffi-
cient sets and prefer four coefficient sets, such as HL13,
LH13, HL13 and LH13 as shown in Figure 1.
Step 4: Divide four coefficient sets such as HL13, LH13,
HL23 and LH23 into 4 × 4 blocks.
Step 5: Present DCT to each block in the chosen sub-
bands (HL13, LH13, HL23 and LH23). These coefficient
sets are chosen to inquire both of imperceptibility and
strength of algorithm equally.
The DWT is very suitable to identify the areas in the
original image where a watermark can be embedded ef-
fectively. This property allows the utilization of the
masking effect of the human visual system such that if a
DWT coefficient is modified, only the region corre-
sponding to that coefficient will be modified. In general
most of the image energy is concentrated at the lower
frequency sub-bands LLx and therefore embedding wa-
termarks in these sub-bands may humiliate the image
appreciably. Embedding in the low frequency sub-bands,
however, could increase robustness appreciably. On the
other hand, the high frequency sub-bands HHx include
the edges and textures of the image and the human eye is
not generally sensitive to changes in such sub-bands.
This allows the watermark to be embedded without being
superficial by the human eye. The compromise accepted
by many DWT-based watermarking algorithm, is to em-
bed the watermark in the middle frequency sub-bands
LHy and HLy where good enough performance of im-
perceptibility and robustness could be achieved.
3. Proposed Watermarking Techniques
We proposed a singular value decomposition technique
and quantization based watermarking technique. The
watermarking techniques can be represented by three
components, L, D and U. It relies on row and column
operations. Row operations involve pre-multiplying ma-
trix and column operations involve post-multiplying ma-
trix. The D component can be explored with a diagonal
matrix. These techniques depend upon the watermark
embedding algorithm and watermark extracting algo-
rithm.
3.1. Watermark Embedding Algorithm
The digital image watermarking algorithm can be fol-
lowed by singular value decomposition techniques, which
involve the characteristics of the D and U components.
In the embedding algorithm, the largest coefficients in D
component were customized and used to embed a wa-
termark. The adaptation was determined by the quantiza-
tion method. We will start the algorithm by applying
SVD transformation on original image and to reconstruct
the watermarked image. Because the largest coefficients
in the D component can oppose with general image
processing, the embedded watermark was not really af-
fected. In this way, the quality of the watermarked image
can be decomposed by quantization method. In our in-
spection, two important features of the D and U compo-
nents are found. In the first feature, the number of non
zero coefficients in the D component could be used to
determine the complexity of a matrix. Commonly, the
greater number of the non-zero coefficient can be speci-
fied by greater complexity. In the second feature, the
relationship between the coefficients in the first column
of the L component could be sealed, when usually image
processing was presented as shown in Figure 3. The
watermarks embedding algorithm can be described as
follows.
Step 1: Read the original host image.
Step 2: Partition the image into blocks of n × n pixels.
Examples: Perform combination of two filters as pre-
filtering operation. The first filter is 3 × 3 sharpening
filter which is defined as Equation (5).
010
13 1
010



(5)
This filter enhances contrast of watermarked image.
The second filter is designed by Laplacian of Gaussian
filter and defined by general equation as 6.

2
2
2
2
1
G
2π
e
x
x
(6)
The Gaussian blur is types of image-blurring filter that
uses a Gaussian function (which also expresses the
normal distribution in statistics) for calculating the
transformation to apply to each pixel in the image as
shown by Equation (7).

22
2
2
2
1
G, e
2π
x
y
xy
(7)
where x is the distance from the origin in the horizontal
axis, y is the distance from the origin in the vertical axis,
and σ is the standard deviation of the Gaussian distribu-
tion. The default value for them in g = 4 and
= 0.5.
Performing these two filters on watermarked image
could caused details of image become more visible, its
means that watermark information which is different
from image background become recognizable uncompli-
catedly.
Step 3: Perform singular value decomposition (SVD)
transformation.
Copyright © 2011 SciRes. JIS
M. THAPA ET AL.
174
Step 4: Extract the greater coefficient D(1, 1) from
each D component and quantize by using a predefined
quantization coefficients A. Suppose that S = D(1, 1)
mod A.
Step 5: Perform embed the two pseudo-random se-
quences PN0, PN1, that is applied to the mid-band coef-
ficients. If A is the matrix of the mid band coefficients of
SVD transformed block, then embedding is done as fol-
lows:
If the watermark bit is 0 then,
D(1, 1) = D(1, 1) + K/4 – A, so that [A < 3K/4]
Otherwise, if the watermark bit is 1 then,
D(1, 1) = D(1, 1) –K/4 + A, so that [A < K/4]
Step 6: Perform the inverse of singular value decom-
position transformation to reform the watermarked im-
age.
3.2. Watermark Extracting Algorithm
The watermark extracting algorithm is similar to the wa-
termark embedding algorithm. Extraction algorithm is
the same as embedding and pre-filtering is used before
applying SVD transform to superior split watermark in-
formation from original image. The watermark extraction
algorithm is performed as described by the following
steps. The first three steps of the watermark extracting
algorithms are same as the watermark embedding algo-
rithm except that the original image is replaced with the
watermarked image. Previously, an embedded block is
detected according to the feature of the D component and
PRNG, the relationship of the U component coefficients
is observed. If a positive relationship is detected, the ex-
tracted watermark has assigned a bit value of 1. Other-
wise, the extracted watermark has assigned a bit value of
0. These extracted bit values convert the original image
SVD from the extracted watermark. The extracted wa-
termark can be specified by original watermarked image
and as shown in Figure 4.
Step 1: Read the watermarked image.
Step 2: watermarked it into blocks of n × n pixels.
Step 3: Perform the SVD transformation.
Step 4: Extract the greater coefficients D''(1, 1) from
each D component and quantize by using a predefined
quantization coefficients A. Suppose that S = D'(1, 1)
mod A.
Step 5: Regenerate the two pseudo random sequences
number using the same key, which is used in the water-
mark embedding algorithm.
Step 6: For an extraction watermark bit valued of zero,
if A < K/2. On the other hand, the extraction watermark
bit value of one, if A > K/2.
Step 7: The watermark is restructured using the ex-
Figure 3. Flow chart for watermark embedding algorithm.
Figure 4. Flow chart for watermark embedding algorithm.
tracted watermark bits, and compute the similarity be-
tween the original watermark and extracted watermarks.
4. Performance Evaluation
We evaluated the performance of the SVD image wa-
termarking algorithms. The performance of the water-
marking methods can be measured by imperceptibility
and robust capabilities. Imperceptibility means that the
superficial quality of the original image should not be
distorted by the presence of watermark image and as
shown by Equation (8). On the other hand, the robustness
is a measure of the intentionally attacks and unintention-
Copyright © 2011 SciRes. JIS
M. THAPA ET AL.
Copyright © 2011 SciRes. JIS
175
ally attacks. It was found that the image quality meas-
ured by peak signal to noise ratio among the water-
marked images was larger than 42 db [15]. This peak
signal to noise ratio is defined as

1
10 2
1
11
255 255
PSNR( ,)10log1
mn
ij
oo
oo
xy


(8)
of 4 × 4 pixels. Each block can be transformed into L, D,
and U components by singular value decomposition. And
then, a set of blocks with the same size as the watermark
was selected, according to the feature of the D compo-
nent. For an embedding watermark block, the relation-
ship between the L component coefficients can be ex-
amined and the coefficients were modified, according to
the watermark to be embedded. In our experiment, the
original image and watermarked image quality is shown
in Figure 5.
The PSNR is employed to evaluate the difference be-
tween an original image o and watermarked image o1.
For the robust capability, mean absolute error (MSE)
measures the difference between an original watermark
W and corresponding extracted watermark W1 as shown
by Equation (9).
We claimed the embedding algorithm and extracting
algorithm to identify the ownership of the original wa-
termarked image as shown in Tables 1 and 2. We can
see that the performance of our algorithm against the
different attacks. Further, the proposed watermarking
algorithm can be used for protecting the copyright of
digital images. It can be observed from Tables 1 and 2
that the future method provides excellent results in the
geometrical attacks.
01
01
0
MSE( , )()
d
i
ww
ww w
(9)
Generally, if PSNR value is larger than 40 db the wa-
termarked image is within acceptable degradation levels,
i.e. the watermarked is almost invisible to human visual
system. A lower mean absolute error reveals that the
extracted watermark w0 resembles the w1 more closely.
The strength of digital watermarking method is accessed
from the watermarked image o1, which is further de-
graded by attacks and the digital watermarking perform-
ance of proposed method is compared with that of Chen
[4]. If a method has a lower MSE(w0, w1), it is more ro-
bust.
Simulation results suggest that this digital watermark-
ing algorithm is robust against many common different
types of attacks such as cropping attacks, pyramid at-
(a) (b) (c)
5. Experimental Results Figure 5. Three original images of 256 × 256 pixels (a) The
original lena image (b) The original facial image (c) The
original moon image.
The experimental results are simulated with the software
MATLAB 7.10 version. It provides a single platform for
computation, visualization, programming and software
development. All problems and solutions in Matlab are
expressed in notation used in linear algebra and essen-
tially involve operations using matrices and vectors. We
are using a 256 × 256 “Lena”, “facial”, and “Moon” as
the gray scale original host image, and a 256 × 256 grey-
scale image of the watermark image. The three images
are shown in Figures 5 and 6 respectively. In the pro-
posed method, we select the largest complexity of blocks;
the original images can be separated into blocks
(a) (b) (c)
Figure 6. Three watermarked images of 256 × 256 pixels (a)
The watermarked lena image; (b) The watermarked facial
image; (c) The watermarked moon image.
Table 1. The experimental results of the error ratio of the embedded watermark after different attacks by proposed method.
Images Type Without Attacks Cropping Attacks Pyramid AttacksRotation AttacksNoise AttacksBlurring Attacks PSNR (DB)
Lena Image 29.2752 38.7569 45.3102 30.0444 26.8879 29.2752 α = 0.2
Facial Image 29.9337 35.2695 46.3784 31.3159 26.5601 29.9337 α = 0.2
Moon Image 24.0375 36.3759 42.8253 26.2853 24.0375 24.0375 α = 0.2
M. THAPA ET AL.
176
Table 2. The experimental results of the error ratio of the extracted watermark after different attacks by the proposed
method.
Images Type Without Attacks Cropping Attacks Pyramid AttacksRotation AttacksNoise AttacksBlurring Attacks PSNR (DB)
Lena Image 272.2211 279.3277 282.3816 273.8656 283.2165 272.2217 α = 0.2
Facial Image 284.1641 283.9997 280.3740 285.8853 279.7679 284.1641 α = 0.2
Moon Image 275.1305 271.2818 287.8321 278.3293 275.1305 275.1305 α = 0.2
tacks, rotation attacks, and noise attacks and blurring at-
tacks Figures 7-12. However, cropping is a geometrical
manipulation and rotation is a geometrical distortion in
practical application. If alpha’s value is more than 0.2 then
quality of original image and watermarked image is not
good. So we are using the dumpy value in these techniques.
We are using the different coefficent of parameters by
proposed method based on digital image watermarking
embedding algorithm and extracting algorithm as a shown
by Tables 3 and 4. Its depends upon the security analysis
and as shown in Figures 13-30. The parameters are to be
satisfying by E1, E2 and E3.
E1 is the parameter of “Lena Image”,
E2 is the parameter of “Facial Image” and
E3 is the parameter of “Moon Image”.
Figure 7. Grey level of original image and watermarked image under without attacks and the corresponding qualities: (a)
Lena (29.2752 db), (272.2211 db), (b) Facial (29.9337 db), (284.1641 db), (c) Moon (24.0375 db), (275.1305 db).
Figure 8. Grey level of original image and watermarked image under cropping attacks and the corresponding qualities: (a)
Lena (38.7569 db), (279.3277 db), (b) Facial (35.2695 db), (283.9997 db), (c) Moon (36.3759 db), (271.2718 db).
Figure 9. Grey level of original image and watermarked image under pyramid attacks and the corresponding qualities: (a)
Lena (45.3102 db), (288.3216 db), (b) Facial (46.3784 db), (280.3740 db) (c) Moon (42.8253 db), (287.8721 db).
Figure 10. Grey level of original image and wtaremarked image under rotation attacks and the corresponding qualities: (a)
Lena (30.0444 db), 273.8656 (db), (b) Facial (31.3159 db), 285.8853 (db) (c) Moon (26.2853 db), 278.3293(db).
Copyright © 2011 SciRes. JIS
177
M. THAPA ET AL.
Figure 11. Grey level of original image and watermarked image under noise attacks and the corresponding qualities: (a) Lena
(26.8879 db), 283.2165 (db) (b) Facial (26.5601 db), 279.7678 (db) (c) Moon (24.0375 db), 275.1305 (db).
Figure 12. Grey level of original image and watermarked image under blurred attacks and the corresponding qualities: (a)
Lena (26.8879 db), 272.2217 (db), (b) Facial (26.5601 db), 284.1641 (db), (c) Moon (24.0375 db), 275.1305 (db).
Table 3. A similarity between coefficients of original image and watermarked image using different parameters by proposed
method under embedded algorithm.
Coefficent of Parameters Without Attacks Cropping Attacks Pyramid AttacksRotation AttacksNoise AttacksBlurring Attacks PSNR (DB)
E1 29.2753 38.7691 29.2752 30.4487 27.4361 23.5507 α = 0.2
E2 29.9334 35.3072 38.4104 10.0200 27.5376 29.9347 α = 0.2
E3 24.0375 36.3290 24.0375 11.1151 12.4233 23.5507 α = 0.2
Table 4. A similarity between coefficients of original image and watermarked image using different parameters by proposed
method under extracted algorithm.
Coefficent of Parameters Without Attacks Cropping Attacks Pyramid AttacksRotation AttacksNoise AttacksBlurring Attacks PSNR (DB)
E1 272.2212 279.3657 272.2212 273.8651 271.7452 272.4247 α = 0.2
E2 241.1641 285.0083 278.8258 284.1675 284.8783 284.1667 α = 0.2
E3 275.1305 291.1681 275.5309 275.1345 273.5305 276.7801 α = 0.2
Figure 13. A similarity between coefficents of original watermarked image under without attacks: (a) Lena without attacks
(29.2753 db), (272.2212 db).
Copyright © 2011 SciRes. JIS
M. THAPA ET AL.
178
Figure 14. A similarity between coefficents of original watermarked image under cropping attacks: (a) Lropped attacks
(38.7691 db, 279.3657db). ena c
Figure 15. A similarity between coefficents of original watermarked image under pyramid attacks: (a) Lena py ramid attacks
(29.2752), (272.2212 db).
Figure 16. A similarity between coefficents of original watermarked image under noise attacks: (a) Noise tacks (27.4361 db),
(271.7452 db). at
Copyright © 2011 SciRes. JIS
179
M. THAPA ET AL.
Fgure 17. A similarity between coefficents of original watermarked image under rotated attacks: (a) Lena rotated attacks
(30.4487 db), (273.865 db).
Figure 18. A similarity between coefficents of original watermarked image under blurring attacks: (a) Lea blurring attacks
(23.5507 db), (272.4247 db). n
Figure 19. A similarity between coefficents of original watermarked image under without attacks: (a) Faal without attacks
(29.9337 db), (284.1641 db). ci
Copyright © 2011 SciRes. JIS
M. THAPA ET AL.
180
Figure 20. A similarity between coefficents of original watermarked image under cropped attacks: (a) Facial cr opped attacks
(35.3072 db), (285.0083 db).
Figure 21. A similarity between coefficents of original watermarked image under pyramid attacks: (a) Facial pyramid attacks
(38.4104 db), (278.8258 db).
Figure 22. A similarity between coefficents of original watermarked image under rotated attacks: (a) Facial otated attacks
(10.0200 db), (284.1675 db). r
Copyright © 2011 SciRes. JIS
181
M. THAPA ET AL.
Figure 23. A similarity between coefficents of original watermarked image under noise attacks: (a) Facial noise attacks
(27.5376 db), (284.8483 db).
Figure 24. A similarity between coefficents of original watermarked image under blurring attacks: (a) Facial brred attacks
(29.9347 db), (284.1667 db). lu
Figure 25. A similarity between coefficents of original watermarked image under without attacks (a) Mooithout attacks
(24.0375 db), (275.1305 db). n w
Copyright © 2011 SciRes. JIS
M. THAPA ET AL.
182
Figure 26. A similarity between coefficents of original watermarked image under cropped attacks: (a) Moon cropped attacks
(36.329 db), (291.1681 db).
Figure 27. A similarity between coefficents of original watermarked image under pyramid attacks: (a) Moonyramid attacks
(24.0375 db), (275.5309 db). p
Figure 28. A similarity between coefficents of original watermarked image under rotated attacks: (a) Mo rotated attacks
(12.4233 db), (273.5305 db). on
Copyright © 2011 SciRes. JIS
M. THAPA ET AL.
Copyright © 2011 SciRes. JIS
183
Figure 29. A similarity between coefficents of original watermarked image under noise attacks: (a) Moon nois attacks
(11.1151 db), (274.1345 db). e
Figure 30. A similarity between coefficents of original watermarked image under without and blurred attacks: (a) Moon
blurred attacks (23.5507 db), (276.7801 db).
decomposition
s one of emerging area of research.
were explored in the proposed technique that provide
In this way, we designed a singular value
algorithm based on digital image watermarking tech-
nique and are to be following by this Figure 31.
6. Conclusions
Digital watermarking i
In this paper, we proposed a digital image watermarking
algorithm based on singular value decomposition. The
algorithm is used for watermarking embedding and wa-
termark extraction. The feautre of the D component and
the realation ship between the U Component coefficents
stronger robustness against different attacks and better
image quality. So, Digital image watermarking tech-
niques are secure on this algorithm. If alpha has a less
than 0.2 value then quality of the original image and wa-
termarked image is good. The experimental results also
recognized the effectiveness of the proposed technique.
Because of these properties, SVD is used for DCT, DFT,
and DWT transformations, and one-way non-symmetri-
cal decomposition. These provide the advantages of
various sizes of transformation and more security. That is
a good performance of the proposed scheme both in
M. THAPA ET AL.
184
Figure 31. SVD algorithm
terms of robustness and security.
d D. Hatzinakos, “Digital Watermarking
Wavelet Decomposition,” Speech
7. References
[1] D. Kundur an
Using Multiresolution
and Signal Processing Proceedings, Acoustics, 1997, pp.
2969-2972.
[2] P. Zeng and C. Jin, “Image Adaptive Watermarking Us-
ing Visual Models,” IEEE Journal on Selected Areas in
Communications, Vol. 16, No. 4, 1998, pp. 525-539.
doi:10.1109/49.668975
[3] L. Rajab, T. Khatib and A. Haj, “Combined DWT-DCT
Digital Image Watermarking,” Journal of Computer Sci-
ence, Vol. 3, 2002, pp. 740-749.
[4] C. C. Chang and P. Tsai, “SVD-based Digital Image Wa-
termarking Scheme,” Pattern Recognition Letters, Vol.
26, No. 10, 2005, pp. 1577-1586.
doi:10.1016/j.patrec.2005.01.004
[5] T. V. Nguyen and J. C. Patra, “A Simple ICA Based Di-
gital Image Watermarking Scheme,” Digital Signal Pro-
cessing, Vol. 18, No. 5, 2007, pp. 762-776.
doi:10.1016/j.dsp.2007.10.004
[6] A. H. Ali and M. Ahmad, “Digital Audio Watermarking
Based on the Discrete Wavelets Transform and Singular
ine, Vol. 18, No. 4, 2001, pp. 33-46.
Value Decomposition,” Europe Journal of Science Re-
search, Vol. 39, No. 1, 2010, pp. 6-21.
[7] C. I. Podilchuk and E. J. Delp, “Digital Watermarking:
Algorithms and Applications,” IEEE Signal Processing
Magaz
doi:10.1109/79.939835
[8] B. Kim, J. G. Choi and D. Min, “Robust Digital Water-
marking Method Against Geometric Attacks,” Real Time
Imaging Processing, Vol. 9, No. 2, 2003, pp. 139-149.
doi:10.1016/S1077-2014(03)00020-2
[9] H. Tina, W. Lu, R. prawn and Y. Ming, “A Fragile Wa-
termarking Scheme for 3D Meshes,” MM-SEC’05, ACM,
pp. 117-123, 2008.
[10] W. Loo and X. Kingsbury, “Digital Watermarking using
. Sukang, “A Digital Watermarking
Complex Wavelets,” International Conference on Image
Processing, Vol. 3, 1999, pp. 29-32.
[11] M. Jiansheng and L
Algorithm Based on DCT and DWT,” International Sym-
posium on Web Information System and Application
(WISA), 2009, pp. 104-107.
[12] A. H. Ali and M. Ahmad, “Digital Audio Watermarking
Based on the Discrete Wavelets Transform and Singular
Value Decomposition,” Europe Journal of Science Re-
search, Vol. 39, No. 1, 2010, pp. 6-21.
[13] W. Lu, H. Lu and F. L. Chung, “Feature Based Water-
marking Using Watermark Template Match,” Applied Ma-
thematics and Computation, Vol. 177, No. 1, 2011, pp.
886-893.
[14] Y. Lu, K. Uehira and K. Yanaka, “Practical Evaluation of
Illumination Watermarking Technique Using Orthogonal
Transforms,” Journal of Display Technology, Vol. 6, No.
9, 2010, pp. 351-358.
doi:10.1109/JDT.2010.2049336
Copyright © 2011 SciRes. JIS