Informatica 41 (2017) 479–493 479
  
A Watermarking Algorithm for Multiple Watermarks Protection Using 
RDWT-SVD and Compressive Sensing 
Rohit Thanki and Vedvyas Dwivedi 
Faculty of Technology and Engineering, C. U. Shah University, Wadhwan City, Gujarat, India 
E-mail: rohitthanki9@gmail.com, vedvyasdwivediphd@gmail.com 
 
Komal Borisagar 
Associate Professor, E.C. Department, Atmiya Institute of Technology & Science, Rajkot, Gujarat, India 
E-mail: krborisagar@aits.edu.in 
 
Surekha Borra 
K.S. Institute of Technology, Bangalore, India 
E-mail: borrasurekha@gmail.com 
  
Keywords: biometrics, color image watermarking, compressive sensing, Compressive Sensing (CS) measurements, 
Redundant Discrete Wavelet Transform (RDWT), Singular Value Decomposition (SVD) 
Received: June 8, 2017 
In this paper, a watermarking algorithm is proposed and analyzed using RDWT-SVD and Compressive 
Sensing for multiple watermarks protection. In this algorithm, the multiple watermarks are inserted into 
single host medium. Here three watermarks are converted into its CS measurements using compressive 
sensing procedure before embedding into host medium. The CS measurements of watermarks are 
generated using discrete cosine transform (DCT) and normal distribution matrix. The singular value of 
these CS measurements of multiple watermarks is inserted into the singular value of approximation 
wavelet coefficients of R channel, G channel and B channel of color host image to get watermarked 
color image. The experimental results show that this proposed algorithm is equally worked for all types 
of watermarks. This proposed algorithm also provides robustness against various watermarking attacks 
and performed better than existed algorithms in the literature. 
Povzetek: Opisani so algoritmi za zaščito vodotiska. 
1 Introduction 
Nowadays, research on digital watermarking is not a new 
phenomenon. Because of various digital watermarking 
algorithms are discussed and proposed by various 
researchers in last fifteen years [1–22]. These algorithms 
are designed for various types of information such as 
digital image, digital video, digital audio, and text. The 
digital watermarking algorithms can be classified into 
various categories based on processing domain, type of 
host medium, type of watermark information and type of 
application. The digital watermarking algorithms based 
on processing domain are divided into the spatial 
domain, transform domain and hybrid domain. In spatial 
domain watermarking, pixel information of host medium 
is modified according to watermark information. In 
transform domain watermarking, frequency coefficients 
of host medium are modified according to watermark 
information. The signal processing transforms such as 
fast Fourier transform (FFT), discrete cosine transform 
(DCT), discrete wavelet transform (DWT), redundant 
discrete wavelet transform (RDWT) and singular value 
decomposition (SVD) are used in transform domain 
based watermarking algorithms. In hybrid domain 
watermarking, hybrid coefficients (which is a 
combination of two or more transform coefficients) of 
host medium is modified according to watermark 
information. The limitation of spatial domain 
watermarking algorithms is that there are not secure 
against any manipulations [1, 21]. The transform domain 
watermarking algorithms have overcome the limitation 
of spatial domain watermarking algorithms but have less 
payload capacity [21]. The hybrid domain watermarking 
algorithms are performed better than spatial and 
transform domain algorithms.  
The digital watermarking algorithms based on the 
type of host medium are divided into image 
watermarking, video watermarking, audio watermarking 
and text watermarking [21]. The digital watermarking 
algorithms based on the type of watermark information 
are divided into image watermarking and biometric 
watermarking [21, 23]. In image watermarking, standard 
image or image of the logo, the text is taken as 
watermark information and inserted into host medium. In 
biometric watermarking, biometric such as fingerprint, 
face, iris, speech, and signature is taken as watermark 
information and inserted into host medium. The digital 
watermarking algorithms based on the type of application 
are divided into robust watermarking and fragile 
watermarking. The robust watermarking is providing 
480 Informatica 41 (2017) 479–493 R. Thanki et al.  
 
protection against any manipulation and used for 
copyright protection of information [2-22]. The fragile 
watermarking is not providing protection but providing 
authentication against any manipulation and used for 
copyright authentication of information [1, 23-24].  
The watermarking algorithms mentioned in [1-24] 
are used single watermark information and inserted into 
host medium. Therefore, the strong need of 
watermarking algorithm is required which can be 
inserted multiple watermarks information into host 
medium. So many researchers are discussed and 
proposed various watermarking algorithms which can be 
inserted multiple watermarks information. This type 
watermarking algorithm is called as multiple 
watermarking algorithms [25]. The multiple 
watermarking algorithms are divided into three types 
such as composite, successive and segmented [25]. The 
composite based multiple watermarking algorithms are 
inserted combined multiple watermarks as a single 
watermark and then inserted into host medium. The 
successive based multiple watermarking algorithms are 
inserted one by one watermark into host medium. The 
segmented based multiple watermarking algorithms are 
inserted multiple watermarks into the specific slot of host 
medium.  
Recently, there are a lot of new watermarking 
algorithms are proposed by researchers for the security of 
various multimedia data. S. Borra and her research team 
proposed a lossless watermarking technique based on 
visual cryptography and central limit theorem for the 
protection of high-resolution images and sensitive 
images [26, 27]. Surekha Borra has also proposed a 
watermarking technique based on visual secret sharing 
for image protection [28]. There are also various new 
watermarking algorithms are proposed by N. Dey and his 
research team for the security of various biometric data 
such as fingerprint, retina, ECG signal and EEG signal 
[29-32]. These algorithms are designed using various 
transforms such as DCT, DWT, and SVD. These 
algorithms are used various approaches such as spread 
spectrum, an edge detection algorithm for achieved better 
results and security to data. In 2016, researchers have 
introduced a watermarking algorithm for 3D images 
protection [33].  
In this paper, a watermarking algorithm is proposed 
for the security of multiple watermarks using stationary 
wavelet transformation (SWT), signature value 
decomposition (SVD) and compressive sensing (CS) 
theory. The SWT is also known as redundant discrete 
wavelet transform (RDWT). This algorithm provides 
protection to multiple watermarks against various 
watermarking attacks. This algorithm can be used for 
security of multiple watermarks transferred over the non-
secure communication channel. The CS theory provides 
security to watermark data before embedding into host 
medium. This step is introduced one additional security 
layer in conventional watermarking approach. This 
algorithm can be used for security of biometric data in 
the multimodal biometric system. Using this algorithm, a 
user can transfer any important multimedia data or 
biometric data over a non-secure channel or between two 
modules of the biometric system. This algorithm can 
provide copyright protection to multimedia data because 
multiple watermarks can be embedded into the host. 
Because imposter can not generate secure watermark 
data without information of orthogonal matrices U, V 
and embedding factor. U, V and embedding factor are 
used as a secret key in this algorithm.      
The rest of paper is organized as follows: in section 
2, related work to proposed algorithm is given. In section 
3, mathematics and information on redundant discrete 
wavelet transform (RDWT); Singular Value 
Decomposition and CS theory are presented. Section 4 
gives information on the implementation of proposed 
algorithm with multiple watermark insertion. The result 
and discussion for robustness and performance of 
proposed algorithm for different watermarks information 
against various watermarking attacks are given in section 
5. Finally, the conclusion is given in section 6.  
2 Related work 
The review on watermarking algorithms mentioned in 
[25, 34–40] is related to proposed watermarking 
algorithm. Authors in [25] proposed a watermarking 
algorithm based on DWT and DCT for multiple 
biometric watermarks insertion. In this algorithm, first 
face information is inserted into host image to get face 
watermarked image. Then speech information is inserted 
into face watermarked image to get face-speech 
watermarked image. Finally, signature information is 
inserted visibly on DCT coefficients of face-speech 
watermarked image to generate multiple watermarks 
based watermarked image. 
Authors in [34] proposed a watermarking algorithm 
based on visual cryptography for multiple watermarks 
insertion. In this algorithm, three watermarks information 
is inserted into Y component of the color image. Authors 
in [35] proposed a watermarking algorithm based on 
SWT and spread spectrum for EMG signal protection. 
Authors in [36] proposed a watermarking algorithm 
based on DWT for two watermarks insertion. In this 
algorithm, two watermarks information is inserted into 
LL subband and HH subband of host image to get 
watermarked image. This algorithm is robust against all 
type watermarking attacks. 
Authors in [37] proposed a watermarking algorithm 
based on RDWT for biometric watermarks. In this 
algorithm, speech watermark information is divided into 
two portions and then inserted into wavelet coefficients 
of red channel and blue channel of the color face image. 
This algorithm provides robustness against watermarking 
attacks. Authors in [38] proposed correlation based and 
spread spectrum based watermarking algorithms for 
multiple watermarks insertion. In this algorithm, first 
watermark information is inserted into host image to get 
the first watermarked image. Then second watermark 
information is inserted into first watermarked image to 
generate multiple watermarks based watermarked image. 
These algorithms are spatial domain algorithms because 
here pixel information of host image is modified 
according to multiple watermarks.  
A Watermarking Algorithm for Multiple Watermarks... Informatica 41 (2017) 479–493 481 
Authors in [39] proposed RDWT and independent 
component analysis (ICA) based watermarking algorithm 
for multiple logo insertions. In this algorithm, multiple 
watermark logos are inserted into LH and HL subbands 
of the host image. This algorithm provides robustness 
against watermarking attacks. Authors in [40] proposed 
DWT and CS theory based watermarking algorithm for 
multiple biometric watermarks insertion. In this 
algorithm, CS measurements of multiple biometric 
watermarks are inserted into HH subband of various 
level of host biometric image. The payload capacity of 
watermarking algorithms mentioned in [34-40] is up to 
50%. 
After discussion on reviewed papers, it is cleared 
that most existed algorithms are based on successive 
based multiple watermarking with having less payload 
capacity. Also, the most of the existed algorithms are 
used grayscale host image for multiple watermarks 
insertion. These existed algorithms are also provided 
copyright protection to multiple watermarks but applied 
on one type of watermark information either image or 
biometrics. Thus, in this paper, a hybrid watermarking 
algorithm is proposed which focuses on high payload 
capacity. The motivation of the present work arises from 
developing a watermarking algorithm which inserted 
multiple watermarks information. In this paper, an 
algorithm is proposed which embeds multiple 
watermarks information in the red channel, green 
channel and blue channel of color host medium. The 
color host image is decomposed using redundant discrete 
wavelet transform (RDWT) and singular value 
decomposition (SVD). We have borrowed the idea from 
[41] with significant improvements in implementation 
and results. The work also goes a step further wherein 
multiple watermarks inserted and combined with 
compressive sensing (CS) theory [42-43].   
In this proposed algorithm, singular value of 
approximation wavelet subband of color host image is 
modified according to the singular value of CS 
measurements of multiple watermarks. This proposed 
algorithm offers good security and high payload 
capacity. In this algorithm, CS theory is applied to DCT 
coefficients of multiple watermarks information which 
are inserted into the color host image. In this proposed 
algorithm, Gaussian measurement matrix A is applied to 
the DCT coefficients of watermark image to get CS 
measurements of watermark image. The proposed 
algorithm is analyzed using various color host images 
and multiple watermarks for different gain factors. The 
orthogonal matching pursuit (OMP) [44] algorithm is 
used for extraction of watermark image from extracted 
CS measurements at detector side. This algorithm is 
selected because it has better computational time and 
easy to implemented.  
3 Preliminaries 
3.1 Redundant Discrete Wavelet 
Transform (RDWT) 
The most common transform such as discrete wavelet 
transform is used for watermarking. But DWT has 
limitation such that downsampling of its subbands [39, 
41]. This is cause payload capacity of watermarking 
algorithms. The DWT is also shift variance which may 
cause a problem in the extraction of watermark 
information. So overcome these limitations of DWT in 
watermarking, researchers are introduced redundant 
discrete wavelet transform (RDWT) for watermarking. 
The RDWT provides shift invariance which is better for 
extraction of watermark information at detector side. The 
RDWT is eliminated downsampling and the upsampling 
process of discrete wavelet transform. This transform 
provides more robust process than DWT. When RDWT 
is applied on any color image which decomposed the 
image into various coefficients are shown in Figure 1.  
The different between discrete wavelet transform 
(DWT) and redundant discrete wavelet transform 
(RDWT) is shown in Figure 2 [39, 41]. 
3.2 Singular Value Decomposition (SVD) 
The singular value decomposition is a linear algebra tool 
which decomposes the image into three different 
matrices such as singular value matrix, two orthogonal 
matrices such as U matrix and V matrix. The singular 
value matrix has non-negative values and diagonally 
place in the matrix. The singular value has sparsity and 
stable property which is suitable for watermarking and 
compression sensing. This value is less effect on human 
visualization capacity when it is modified. When SVD is 
applied on any image is shown in Figure 3.  
3.3 Compressive Sensing (CS) theory 
An image f can become sparse image when only a few 
non-zero elements are presented in the image. The image 
f can be converted into a sparse image by applying image 
 
(a) Original Image 
 
(b) Wavelet coefficients of 
Red Channel 
 
(c) Wavelet coefficients of 
Green Channel 
 
(d) Wavelet coefficients of 
Blue Channel 
Figure 1: Wavelet Coefficients of RDWT for Color 
Image. 
482 Informatica 41 (2017) 479–493 R. Thanki et al.  
 
transform basis matrix. The image has x non-zero 
coefficients (sparse coefficients) are represented as  
)( fx                                                                        (1) 
where x is the sparse coefficients,  is the transform 
basis matrix.  
The CS measurements of image using compressive 
sensing represented by using following equation [42, 43] 
xAy                                                                  (2) 
Where y is the sparse measurements, A is known as 
measurement matrix.  
To reconstruction of an image from CS 
measurements, various CS recovery algorithms are 
available in the literature [42-45]. A greedy algorithm 
such as orthogonal matching pursuit (OMP) is used 
which is introduced and designed by Tropp et al. [44]. 
The more detail on OMP algorithm is given in next 
subsection. It is used in this paper for the extraction of 
sparse coefficients from CS measurements. It can be 
mathematically explained using below equation: 
),(* AyOMPx                                                      (3) 
Where x* is extracted sparse coefficients which are 
extracted from the CS measurements y. 
3.4 Orthogonal Matching Pursuit (OMP) 
algorithm 
The Orthogonal Matching pursuit (OMP) algorithm is 
introduced and designed by J. Tropp and A. Gilbert in 
2007 [44]. This algorithm is a greedy algorithm which is 
used for extraction of sparse coefficients from the CS 
measurements. The OMP algorithm is defined by three 
basic steps such as matching, orthogonal projection, and 
residual updating. The output of OMP algorithm is one 
non-zero sparse coefficient in each iteration. The OMP 
algorithm extracted sparse coefficients x from y=Ax. The 
mathematical steps for OMP algorithm are described in 
below steps: 
 Input:  CS measurements y, Measurement matrix A  
 Initialization: index I = A, residual r = y, sparse 
representation   = 0  Rm. 
 Step 1: Initialize the residual r0 = y and initialized 
the set of selected variable x(c0) =. Let iteration 
counter i = 1. 
1
'
max
i
rtxt                                                          (4) 
 Step 2: Find the variable xt that solves the 
maximization problem below using equation (4) and 
add the variable xti to the set of selected variables. 
Update Ci using equation (5). 
 
(a) 
 
(b) 
Figure 2: (a) DWT Analysis and Synthesis for Image (b) RDWT analysis and Synthesis for Image. 
 
Figure 3: SVD Matrices of Image: U Matrix (left),  
S Matrix (middle), V Matrix (right). 
A Watermarking Algorithm for Multiple Watermarks... Informatica 41 (2017) 479–493 483 
}{1 itiCiC 
                                                  (5) 
 Step 3: Let Pi which is given below equation (6) 
denote the projection onto the linear space spanned 
by the elements of x(ci). Then update residual r using 
equation (7).  
'
)(
1
))(
'
)()(( iCxiCxiCxiCxiP

                      (6) 
yiPIir )(                                                          (7) 
 Step 4: It the stopping condition is achieved, stop 
the algorithm. Otherwise, set i = i +1 and return to 
step 2. 
 Output: Sparse Coefficients x.  
 
The solution of equation 6 is getting by least square 
optimization method. The value of projection Pi is taken 
as extracted sparse coefficients x. The value of extracted 
sparse coefficients depends on linear projection between 
CS measurements vector and measurement matrix-
vector. When both vectors have equal value then the 
output is zero because the projection is zero. So every 
time, the output of OMP algorithm is non-zero 
coefficients. 
4 Watermarking algorithm using 
RDWT-SVD and CS theory  
Today’s world, the data size of multimedia is also 
increasing day by day. Also, the existed watermarking 
techniques are embedded multiple watermarks 
information in host image but the size of watermark 
information is few bits or less than the size of the host 
image. Thus, the new watermarking algorithm should 
have more secure and higher payload capacity. So, in the 
proposed algorithm CS is used for providing security to 
multiple watermarks information before embedding. In 
the proposed algorithm, redundant DWT is applied on 
RGB channel of color host image to get wavelet 
coefficients of RGB channel of the color host image. 
Then SVD is applied on these coefficients to get the 
singular value of RGB channel of the color host image. 
Then the singular value of approximation subbands is 
chosen for CS measurements embedding because of 
these coefficients are less effect by watermarking attacks. 
The CS measurements of multiple watermarks 
information are generated using DCT and Gaussian 
measurement matrix. In this section, the multiple 
watermark embedding procedure and extraction 
procedure of proposed algorithm are described. 
4.1 Multiple watermark embedding 
procedure 
The multiple watermark images are transformed into the 
sparse domain using discrete cosine transform (DCT). 
The CS measurements y of watermark images is 
generated using compressive sensing with Gaussian 
measurement matrix. The singular value of CS 
measurements y of watermark images is embedded into 
the singular value of LL subband of RGB channel of the 
color host image. Figure 4 shows the framework for the 
proposed embedding procedure and the mathematical 
steps for multiple watermark embedding are given 
below. 
 Take multiple watermarks w1, w2, w3 and compute 
the size of watermarks. Apply DCT on watermark 1, 
watermark 2 and watermark 3 to get DCT 
coefficients of watermark 1, watermark 2 and 
watermark 3, respectively. 
)3(3
)2(2
)1(1
wdctD
wdctD
wdctD



                                                        (8) 
In above equation, w1, w2, and w3 are watermark 1, 
watermark 2 and watermark 3, respectively; D1, D2, 
and D3 are DCT coefficients of watermark 1, 
watermark 2 and watermark 3, respectively. 
 Then generate CS measurements of watermark 1, 
watermark 2 and watermark 3 using Compressive 
sensing procedure. The Gaussian measurement 
matrix is generated using zero mean and one 
variance.  
33
22
11
DAy
DAy
DAy



                                                        (9) 
In above equation, y1, y2, and y3 are CS 
measurements of watermark 1, watermark 2 and 
watermark 3, respectively; A is Gaussian 
measurement matrix; D1, D2, and D3 are DCT 
coefficients of watermark 1, watermark 2 and 
watermark 3, respectively. 
 Apply SVD on CS measurements of watermark 1, 
watermark 2 and watermark 3 to get the singular 
value of CS measurements of watermark 1, 
watermark 2 and watermark 3, respectively. 
)3(]3,3,3[
)2(]2,2,2[
)
1
(]
1
,
1
,
1
[
ysvdYVYSYU
ysvdYVYSYU
ysvd
Y
V
Y
S
Y
U



                            (10) 
In above equation, SY1, SY2, and SY3 are the singular 
value of CS measurements of watermark 1, 
watermark 2 and watermark 3, respectively; y1, y2, 
and y3 are CS measurements of watermark 1, 
watermark 2 and watermark 3, respectively.
 
 Take color host image IH and compute the size of 
the host image. Then color image decomposed into 
R channel, G channel and B channel.  
 Apply RDWT on R channel, G channel, and B 
channel of host image to get wavelet coefficients of 
the host image.  
484 Informatica 41 (2017) 479–493 R. Thanki et al.  
 
)_(]3,3,3,3[
)_(]2,2,2,2[
)Re_(]1,1,1,1[
BlueIHRDWTHHHLLHLL
GreenIHRDWTHHHLLHLL
dIHRDWTHHHLLHLL



 (11) 
In above equation, IH_Red, IH_Green, and IH_Blue 
are R channel, G channel and B channel of the color 
host image, respectively. 
 Then SVD is applied on LL subband to get the 
singular value of approximation wavelet coefficients 
of the host image.  
)3(]_,_,_[
)2(]_,_,_[
)1(]_,_,_[
LLsvdVBIHSBIHUBIH
LLsvdVGIHSGIHUGIH
LLsvdVRIHSRIHURIH



      (12) 
In above equation, IHR_S, IHG_S, and IHB_S are the 
singular value of LL subband of R channel, G 
channel and B channel of the color host image, 
respectively. 
 The singular value of approximation wavelet 
coefficients of host image is modified according to 
the singular value of CS measurements of 
watermark1, watermark 2 and watermark 2 using 
gain factor. 
)
3
(_3
)2(_2
)
1
(_1
Y
SkSBIHS
YSkSGIHS
Y
SkSRIHS



                                (13) 
In above equation, S1, S2, and S3 have modified the 
singular value of LL subband of R channel, G 
channel and B channel of the color host image, 
respectively; k is a gain factor. 
 Apply inverse SVD on modified singular value to 
get modified LL subband of R channel, G channel 
and B channel of the color host image.  
'
_3_3_
'
_2_2_
'
_1_1_
VBIHSUBIHLLnew
VGIHSUGIHLLnew
VRIHSURIHLLnew



            (14) 
 
Figure 4: Framework for Proposed Embedding Procedure. 
A Watermarking Algorithm for Multiple Watermarks... Informatica 41 (2017) 479–493 485 
In above equation, new_LL1, new_ LL2, and 
new_LL3 have modified LL subband of R channel, 
G channel and B channel of the color host image, 
respectively. 
 The inverse RDWT is applied on modified LL 
subband with unmodified subbands to get modified 
R channel, G channel and B channel of the color 
host image.  
)3,3,3,3_(_
)2,2,2,2_(_
)1,1,1,1_(_
HHHLLHLLnewIRDWTBWA
HHHLLHLLnewIRDWTGWA
HHHLLHLLnewIRDWTRWA



(15) 
In above equation, WA_R, WA_G, and WA_B are 
modified R channel, G channel and B channel of the 
color host image, respectively. 
 Finally, the color image reconstruction is applied on 
modified RGB channels to get watermarked color 
image IW. 
 
4.2 Multiple watermark extraction 
procedure 
For extraction of the watermark, from watermarked 
image, the measurement matrix, orthogonal matrices U, 
V of CS measurements is required. The OMP algorithm 
[44] is used to the extraction of DCT coefficients of 
multiple watermarks from its extracted CS measurements 
values. Figure 5 shows the block diagram for the 
proposed extraction procedure and the mathematical 
steps for watermark extraction are given below. 
 Take watermarked image and decomposed into R 
channel, G channel, and B channel. Apply redundant 
DWT on the watermarked image IW, to get the 
modified wavelet coefficients of R channel, G 
channel and B channel, respectively.  
)(]3,3,3,3[
)(]2,2,2,2[
)(]1,1,1,1[
IWBRDWTHHWHLWLHWLLW
IWGRDWTHHWHLWLHWLLW
IWRRDWTHHWHLWLHWLLW



(16) 
  
Figure 5: Framework for Proposed Extraction Procedure. 
486 Informatica 41 (2017) 479–493 R. Thanki et al.  
 
In above equation, IWR, IWG, and IWB are R 
channel, G channel and B channel of color 
watermarked image, respectively. 
 Apply SVD on LL subband of R channel, G channel 
and B channel of watermarked image to get the 
singular value of R channel, G channel and B 
channel of watermarked image.  
)3(]_,_,_[
)2(]_,_,_[
)1(]_,_,_[
LLWsvdVBIWSBIWUBIW
LLWsvdVGIWSGIWUGIW
LLWsvdVRIWSRIWURIW



   
(17) 
In above equation, IWR_S, IWG_S, and IWB_S are the 
singular value of LL subband of R channel, G 
channel and B channel of color watermarked image, 
respectively. 
 Extract singular value of CS measurements of 
multiple watermarks using singular value of RGB 
channel of host image and singular value of RGB 
channel of watermarked image with help of gain 
factor as  
kS
B
IHSBIWY
S
kS
G
IHS
G
IW
Y
S
kS
R
IHSRIWY
S
/)__(
3
/)__(
2
/)__(
1






                       (18) 
In above equation, S*Y1, S*Y2, and S*Y3 are extracted 
singular value of CS measurements of watermark 1, 
watermark 2 and watermark 3, respectively. 
 Then apply inverse SVD on extracted singular value 
with original U, V to get extracted CS measurements 
of multiple watermarks information.  
'
_3_3_33
'
_2_2_22
'
_1_1_11
VWSEWUWEy
VWSEWUWEy
VWSEWUWEy



                  (19) 
In above equation, Ey1, Ey2, and Ey3 are extracted 
CS measurements of watermark 1, watermark 2 and 
watermark 3, respectively. 
 The OMP algorithm is applied on extracted CS 
measurements of multiple watermarks information 
to get DCT coefficients of multiple watermarks. 
),3(
*
3
),2(
*
2
),1(
*
1
AEyOMPD
AEyOMPD
AEyOMPD



                                            (20) 
In above equation, D*1, D*2, and D*3 have extracted 
DCT coefficients of watermark 1, watermark 2 and 
watermark 3, respectively; A is a measurement 
matrix. 
 Finally applied inverse DCT on extracted DCT 
coefficients to get multiple watermarks at detector 
side.  
)
*
3(
*
3
)
*
2(
*
2
)
*
1(
*
1
Didctw
Didctw
Didctw



                                                    (21) 
In above equation, w*1, w*2, and w*3 are extracted 
watermark 1, extracted watermark 2 and extracted 
watermark 3 at detector side, respectively. 
5 Results and discussion 
The testing of proposed algorithm using various types of 
images with quality measures are discussed in this 
section. The various test images and watermarks are 
discussed in subsection 5.1. The quality measures such as 
PSNR, NCC, and payload capacity for proposed 
algorithm is discussed in subsection 5.2. The 
performance analysis of proposed algorithm for multiple 
watermarks is discussed in subsection 5.3. The 
performance analysis of proposed algorithm for multiple 
biometric watermarks is discussed in subsection 5.4. The 
comparison of proposed algorithm with existed 
algorithms is discussed in subsection 5.5. 
5.1 Test images and watermarks 
The performance of any watermarking scheme varies 
with different types of images. Therefore, in this paper, 
two different types of host color images such as Lena 
image and mandril image are used. In Figure 6, Lean 
host image and mandril host image have a size of 
176176 pixels and 128128 pixels, respectively. The 
various type of watermarks information is taken in this 
paper. Figure 7 shows various standard watermark 
images with various frequency coefficients. The 
cameraman watermark image (which has low frequency 
coefficients), peppers watermark image (which has 
middle-frequency coefficients) and Goldhill watermark 
image (which has high frequency coefficients) have a 
size of 176176 pixels. Figure 8 shows various biometric 
watermarks images. The fingerprint watermark image, 
iris watermark image, and sign watermark image have a 
size of 128128 pixels. 
The performance of proposed algorithm is carried 
out for different gain factor. The analysis of proposed 
algorithm is carried out for various watermarking attacks 
such as JPEG compression; noise addition such as 
Gaussian noise, Salt-Pepper noise, and speckle noise; 
 
(a) 
 
(b) 
Figure 6: Test Host Color Images (a) Lena (b) Mandril. 
A Watermarking Algorithm for Multiple Watermarks... Informatica 41 (2017) 479–493 487 
filter attacks such as Mean, median, sharpen and 
Gaussian low pass filter; geometric attacks such as 
histogram equalization, rotation, and cropping.  
5.2 Quality measures 
The perceptual quality of watermarked image is 
measured by Peak Signal to Noise Ratio (PSNR) [46] 
and the mathematical equation of PSNR is given in 
below 
MSE
PSNR
2
255
10
log10                                              (22) 
In above equation, MSE is defined as mean square 
error and given by 








M
x
N
y
yxIyxI
NM
MSE
1 1
2
)},(),({
1
          (23) 
In above equation, I and I* is original host image and 
watermarked image respectively. 
The MSE is measured in general scale while PSNR 
is measured in logarithmic scale. The high value of 
PSNR is indicated more imperceptibility of 
watermarking scheme. The normalized cross correlation 
(NCC) [46] is used to measure the similarity between 
original watermark image and extracted watermark 
image. The mathematic equation for NCC is given in 
below 










M
x
N
y
yxw
M
x
N
y
yxwyxw
NCC
1 1
),(
2
1 1
),(),(
                          (24) 
In above equation, w is original watermark image 
and w* is extracted watermark image. 
The NCC value lies in 0 to 1. When NCC value is 1 
then it is indicated the extracted watermark image is 
exactly similar to the original watermark image. But 
NCC value is 0 then it is indicated that the extracted 
watermark image is not similar to the original 
watermark image. In this paper, PSNR is used for 
measurement of imperceptibility of proposed 
watermarking algorithm. NCC is used for measurement 
of robustness and security of proposed watermarking 
algorithm.  
The payload capacity of any watermarking system 
can be defined as the amount of watermark information 
is embedded into host medium. The payload capacity 
can be calculated by a number of bits embedded in host 
pixels or a ratio of the size of the watermark to the size 
of host medium.  In this paper, payload capacity is 
calculated using below equation.  
SizeofHost
rmarkSizeofWate
PC                                             (25) 
In above equation, PC is payload capacity of the 
watermarking algorithm; the size of watermark and host 
in pixels. 
5.3 Performance analysis of proposed 
algorithm for multiple watermarks 
In the proposed algorithm, CS measurements are 
generated using DCT coefficients of multiple watermark 
images. In this proposed algorithm, the wavelet 
coefficients of R channel, G channel and B channel of 
color host image are generated using db1 or haar 
wavelet. The db1 or haar wavelet is basic wavelet, 
simplest, asymmetric and orthogonal as well as bi-
orthogonal in nature. These wavelets are commonly used 
in watermarking. The singular value of CS measurements 
 
(a) 
 
(b) 
 
 
(c) 
Figure 7: Test Standard Watermark Images 
 (a) Cameraman (b) Peppers (c) Goldhill. 
 
(a) 
 
(b) 
 
 
(c) 
Figure 8: Test Biometric Watermark Images  
(a) Fingerprint (b) Iris (c) Sign. 
488 Informatica 41 (2017) 479–493 R. Thanki et al.  
 
is embedded into the singular value of LL subband of the 
color host image.  
Figure 9 shows the watermarked Lena host image 
with multiple watermarks and extracted standard 
watermark images without application of watermarking 
attacks on watermarked color image using gain factor 
0.002 and db1 wavelet.  
Table 1 shows PSNR and NCC values for multiple 
standard watermark images for different gain factor 
without application of watermarking attacks. The 
performance analysis of proposed algorithm for multiple 
standard watermark images against various watermarking 
attacks is carried out for different gain factor. Table 2 
shows the performance of proposed algorithm for 
multiple standard watermark images under JPEG 
compression and Gaussian noise addition attack.   
Table 3 shows the performance of proposed 
algorithm for multiple standard watermark images under 
noise addition attacks such as salt-pepper noise and 
speckle noise. Table 4 shows the performance of 
proposed algorithm for multiple standard watermark 
images under various filters such as median and mean. 
Table 5 shows the performance of proposed 
algorithm for multiple standard watermark images 
under Gaussian low pass filter attack and sharpening 
attack. Table 6 and 7 shows the performance of 
proposed algorithm for multiple standard watermark 
 
(a) Watermarked Lena 
Host Image with 
Multiple Standard 
Watermark Images 
 
 
(b) Extracted 
Cameraman Watermark 
Image 
 
(c) Extracted Peppers 
Watermark Image 
 
(d) Extracted Goldhill 
Watermark Image 
Figure 9: Results of Proposed Algorithm for Multiple 
Standard Watermark Images. 
Table 1: PSNR and NCC values of Proposed 
Algorithm for Multiple Standard Watermark 
Images without Application of Watermarking 
Attacks. 
Gain 
Factor 
PSNR 
(dB) 
NCC 1 for 
Cameraman 
Watermark 
Image 
NCC 2 for 
Peppers 
Watermark 
Image 
NCC 3 for 
Goldhill 
Watermark 
Image 
0.002 43.56 0.9703 0.9822 0.9963 
0.003 39.94 0.9555 0.9925 0.9963 
0.004 37.44 0.9650 0.9897 0.9904 
0.005 34.77 0.9636 0.9927 0.9861 
 
Table 2: Performance of Proposed Algorithm for 
Multiple Standard Watermark Images under JPEG 
Compression Attack and Gaussian Noise Addition 
Attack. 
Gain 
Factor 
JPEG Compression (Q = 
50) 
Gaussian Noise (Variance 
= 0.001) 
 NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9693 0.9844 0.9884 0.9785 0.9917 0.9932 
0.003 0.9627 0.9927 0.9900 0.9595 0.9953 0.9947 
0.004 0.9644 0.9889 0.9893 0.9657 0.9924 0.9833 
0.005 0.9690 0.9921 0.9929 0.9637 0.9903 0.9904 
Table 3: Performance of Proposed Algorithm for 
Multiple Standard Watermark Images under JPEG 
Compression Attack and Salt-Pepper Noise and Speckle 
Noise Addition Attack. 
Gain 
Factor 
Salt-Pepper Noise 
(Variance = 0.005) 
Speckle Noise (Variance = 
0.004) 
NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9585 0.9843 1.0000 0.9674 0.9876 0.9987 
0.003 0.9564 0.9907 0.9874 0.9742 0.9888 0.9946 
0.004 0.9656 0.9904 0.9884 0.9662 0.9880 0.9905 
0.005 0.9676 0.9904 0.9875 0.9694 0.9925 0.9873 
Table 4: Performance of Proposed Algorithm for 
Multiple Standard Watermark Images under Median 
Filter Attack and Mean Filter Attack. 
Gain 
Factor 
Median Filter (Size of 
Filter Mask = 33) 
Mean Filter (Size of Filter 
Mask = 33) 
 NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9584 0.9831 0.9948 0.9864 0.9934 0.9914 
0.003 0.9614 0.9930 0.9975 0.9599 0.9939 0.9962 
0.004 0.9664 0.9895 0.9918 0.9642 0.9941 0.9966 
0.005 0.9636 0.9941 0.9923 0.9665 0.9884 0.9847 
Table 5: Performance of Proposed Algorithm for 
Multiple Standard Watermark Images under Gaussian 
Low Pass Filter Attack and Sharpening Attack. 
Gain 
Factor 
Gaussian Low Pass Filter 
(Size of Filter Mask = 
33) 
Sharpening Attack 
 NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9596 0.9798 0.9963 0.9461 0.9804 0.9989 
0.003 0.9572 0.9914 0.9874 0.9577 0.9900 0.9898 
0.004 0.9658 0.9854 0.9846 0.9693 0.9872 0.9908 
0.005 0.9668 0.9917 0.9864 0.9674 0.9938 0.9888 
 
A Watermarking Algorithm for Multiple Watermarks... Informatica 41 (2017) 479–493 489 
images under rotation attack, cropping attack, and 
histogram equalization attack.  
The NCC value is above 0.94 shown in Table 2 to 7 
in the case of different type of watermarking attacks for 
color host image with multiple standard watermark 
images. This situation indicated that the algorithm can 
provide robustness against various type of watermarking 
attacks for multiple standard watermark images.  
5.4 Performance analysis of proposed 
algorithm for multiple biometric 
watermarks 
In the proposed algorithm, CS measurements are 
generated using DCT coefficients of multiple biometric 
watermark images. In this proposed algorithm, the 
wavelet coefficients of R channel, G channel and B 
channel of color host image are generated using db1 
wavelet. The singular value of CS measurements is 
embedded into the singular value of LL subband of the 
color host image.  
Figure 10 shows the watermarked mandril host 
image with multiple biometric watermarks and extracted 
biometric watermark images without application of 
watermarking attacks on watermarked color image using 
embedding factor 0.002 and db1 wavelet.  
Table 8 shows PSNR and NCC values for multiple 
biometric watermarks for different embedding factor 
without application of watermarking attacks. The 
performance analysis of proposed algorithm for multiple 
biometric watermark images against various 
watermarking attacks is carried out for different gain 
factor. Table 9 shows the performance of proposed 
algorithm for multiple biometric watermark images 
under JPEG compression and Gaussian noise addition 
attack.   
Table 10 shows the performance of proposed 
algorithm for multiple biometric watermark images 
under noise addition attacks such as salt-pepper noise 
and speckle noise. Table 11 shows the performance of 
proposed algorithm for multiple biometric watermark 
images under various filters such as median and mean. 
Table 12 shows the performance of proposed 
algorithm for multiple biometric watermark images 
under Gaussian low pass filter attack and sharpening 
Table 9: Performance of Proposed Algorithm for 
Multiple Standard Watermark Images under Rotation 
Attack and Cropping Attack. 
Gain 
Factor 
Rotation Attack Cropping Attack 
 NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9864 0.9891 0.9929 0.9633 0.9804 0.9974 
0.003 0.9749 0.9884 0.9878 0.9651 0.9920 0.9891 
0.004 0.9679 0.9913 0.9958 0.9666 0.9879 0.9908 
0.005 0.9637 0.9901 0.9813 0.9657 0.9932 0.9951 
Table 10: Performance of Proposed Algorithm for 
Multiple Standard Watermark Images under Histogram 
Equalization Attack. 
Gain 
Factor 
Histogram Equalization 
Attack 
 NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9400 0.9829 0.9989 
0.003 0.9604 0.9919 0.9890 
0.004 0.9710 0.9950 0.9855 
0.005 0.9677 0.9919 0.9905 
Table 11: PSNR and NCC values of Proposed 
Algorithm for Multiple Biometric Watermark Images 
without Application of Watermarking Attacks. 
Gain 
Factor 
PSNR 
(dB) 
NCC 1 for 
Fingerprint 
Watermark 
Image 
NCC 2 for 
Iris 
Watermark 
Image 
NCC 3 for 
Sign 
Watermark 
Image 
0.002 43.15 0.9509 0.9993 0.9714 
0.003 39.17 0.9612 1.0000 0.9726 
0.004 36.43 0.9653 0.9981 0.9717 
0.005 34.19 0.9601 0.9990 0.9700 
Table 12: Performance of Proposed Algorithm for 
Multiple Biometric Watermark Images under JPEG 
Compression Attack and Gaussian Noise Addition 
Attack. 
Gain 
Factor 
JPEG Compression (Q = 
50) 
Gaussian Noise (Variance 
= 0.001) 
 NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9474 0.9919 0.9753 0.9435 0.9946 0.9742 
0.003 0.9603 1.0000 0.9747 0.9595 1.0000 0.9763 
0.004 0.9631 0.9992 0.9726 0.9672 0.9988 0.9742 
0.005 0.9572 0.9971 0.9689 0.9599 0.9961 0.9696 
 
 
(a) Watermarked 
Mandril Host Image 
with Multiple Biometric 
Watermark Images 
 
(b) Extracted 
Fingerprint Watermark 
Image 
 
(c) Extracted Iris 
Watermark Image 
 
(d) Extracted Sign 
Watermark Image 
Figure 10: Results of Proposed Algorithm for Multiple 
Biometric Watermark Images. 
490 Informatica 41 (2017) 479–493 R. Thanki et al.  
 
attack. Table 13 and 14 shows the performance of 
proposed algorithm for multiple biometric watermark 
images under rotation attack, cropping attack, and 
histogram equalization attack.  
The NCC value is above 0.94 shown in Table 9 to 14 
in the case of different type of watermarking attacks for 
color host image with multiple biometric watermark 
images. This situation indicated that the algorithm can 
provide robustness against various type of watermarking 
attacks for multiple biometric watermark images.   
After obtaining results of proposed algorithm for 
multiple watermarks, it is clearing seen that this 
algorithm can be applied any type of watermark 
information. The NCC value obtained for various 
watermarking attacks are above 0.90 which is indicated 
this algorithm provides robustness against any 
manipulation. This situation indicated that this algorithm 
can be used for security of multimedia data when it is 
transferred over a non-secure channel. This algorithm is 
also used in a multimodal biometric system where 
multiple biometric data can be secure when it is 
transferred over a communication channel between two 
modules. 
5.5 Comparison of proposed algorithm 
with existed algorithms 
The comparison of proposed algorithm with existed 
algorithms with various features is given in Table 15. 
Table 13: Performance of Proposed Algorithm for 
Multiple Biometric Watermark Images under JPEG 
Compression Attack and Salt-Pepper Noise and 
Speckle Noise Addition Attack. 
Gain 
Factor 
Salt-Pepper Noise 
(Variance = 0.005) 
Speckle Noise (Variance = 
0.004) 
NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9436 0.9985 0.9725 0.9498 0.9959 0.9772 
0.003 0.9598 0.9897 0.9725 0.9603 1.0000 0.9745 
0.004 0.9622 0.9983 0.9712 0.9628 0.9956 0.9713 
0.005 0.9583 0.9994 0.9677 0.9589 0.9951 0.9707 
Table 14: Performance of Proposed Algorithm for 
Multiple Biometric Watermark Images under Median 
Filter Attack and Mean Filter Attack. 
Gain 
Factor 
Median Filter (Size of 
Filter Mask = 33) 
Mean Filter (Size of Filter 
Mask = 33) 
 NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9460 1.0000 0.9731 0.9447 1.0000 0.9718 
0.003 0.9572 1.0000 0.9741 0.9592 0.9908 0.9738 
0.004 0.9640 0.9971 0.9724 0.9634 0.9962 0.9728 
0.005 0.9579 0.9942 0.9658 0.9586 1.0000 0.9695 
Table 15: Performance of Proposed Algorithm for 
Multiple Biometric Watermark Images under Gaussian 
Low Pass Filter Attack and Sharpening Attack. 
Gain 
Factor 
Gaussian Low Pass Filter 
(Size of Filter Mask = 
33) 
Sharpening Attack 
 NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9443 1.0000 0.9741 0.9534 1.0000 0.9755 
0.003 0.9591 1.0000 0.9746 0.9565 1.0000 0.9735 
0.004 0.9604 1.0000 0.9705 0.9621 0.9973 0.9706 
0.005 0.9556 0.9945 0.9712 0.9566 0.9996 0.9708 
Table 16: Performance of Proposed Algorithm for 
Multiple Biometric Watermark Images under Rotation 
Attack and Cropping Attack. 
Gain 
Factor 
Rotation Attack Cropping Attack 
 NCC 
1 
NCC 
2 
NCC 
3 
NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9512 0.9949 0.9749 0.9455 1.0000 0.9717 
0.003 0.9630 0.9955 0.9719 0.9640 1.0000 0.9736 
0.004 0.9646 0.9998 0.9713 0.9615 0.9950 0.9715 
0.005 0.9577 0.9995 0.9678 0.9545 0.9998 0.9669 
 
Table 17: Performance of Proposed Algorithm for 
Multiple Biometric Watermark Images under Rotation 
Attack and Cropping Attack. 
Gain 
Factor 
Histogram Equalization 
Attack 
 NCC 
1 
NCC 
2 
NCC 
3 
0.002 0.9423 1.0000 0.9730 
0.003 0.9601 1.0000 0.9721 
0.004 0.9616 0.9940 0.9725 
0.005 0.9570 1.0000 0.9693 
Table 18: Comparison of Proposed Algorithm with 
Existed Algorithms. 
Techniques 
Existed 
Algorithm in 
[25] 
Existed 
Algorithm in 
[41] 
Proposed 
Algorithm 
Type of 
Multiple 
Watermarking 
Successive - Composite 
Host Medium 
Grayscale 
Image 
Grayscale 
Image 
Color Image 
Type of 
Watermark 
Information 
Face, Speech, 
and Sign 
Grayscale 
Image 
Grayscale 
Image, 
Fingerprint, Iris 
and Sign 
Number of 
Watermarks 
Three Single Three 
Used Signal 
Processing 
Transform 
DWT and 
DCT 
RDWT and 
SVD 
RDWT, SVD, 
and DCT 
CS theory is 
applied on 
Watermark 
Image 
No No Yes 
PSNR (dB) 33.2819 37.52 43.56 
 
A Watermarking Algorithm for Multiple Watermarks... Informatica 41 (2017) 479–493 491 
The comparison shows that the CS theory is applied 
on multiple watermarks in proposed algorithm before 
embedding which is not applied in existed algorithms. 
The PSNR value of proposed algorithm is better than 
existed algorithms in the literature. The proposed 
algorithm can be used for any multiple watermarks such 
as standard images and biometric images while algorithm 
in [25] is used for biometric images and algorithm in [41] 
is used for the standard image.   
6 Conclusion 
Nowadays, the payload capacity and multiple 
watermarks inserted ability of watermarking algorithms 
are considered as important parameters. So in this paper, 
multiple watermarks based watermarking algorithm with 
high payload capacity is presented. A watermarking 
algorithm using redundant DWT, SVD and compressive 
sensing for multiple watermarks protection is designed 
and analyzed for the security of multiple watermarks. 
The proposed algorithm is also shown application on CS 
theory for generation of multiple CS measurements of 
watermark image. The CS theory is providing protection 
to multiple watermark images before embedding in 
proposed algorithm. Our algorithm is flexible and may be 
used for any type of watermark information such as 
standard image and biometric image. This proposed 
algorithm can be used for security of multiple watermark 
information when it is transferred over the non-secure 
communication channel.  
An experimental is implemented using various color 
host images and watermarks information. Two types of 
host images and six types of watermark images are used 
in the experiments. The proposed algorithm has generally 
overcome the limitation of existed watermarking 
algorithms. The experiments also show that the proposed 
algorithm can provide robustness against various 
watermarking attacks such as JPEG compression, the 
addition of noise, filter, sharpening, histogram 
equalization, geometric attack such as rotation, cropping. 
The proposed algorithm is performed better than existed 
algorithms available in the literature based on PSNR 
values.   
The limitation of proposed algorithm is that original 
host image is required at detector side for extraction of 
watermark information. So this algorithm is a non-blind 
watermarking algorithm in nature. In the future, the 
algorithm is designed and analyzed for various data such 
as speech signal, ECG signal, digital video and digital 
audio signal. Also, a hardware implementation of 
proposed algorithm is designed using various DSP 
platform and FPGA kits.  
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