Image watermarking based on integer wavelet transform-singular value decomposition with variance pixels

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 9, No. 3, June 2019, pp. 2185~2195
ISSN: 2088-8708, DOI: 10.11591/ijece.v9i3.pp2185-2195  2185
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Image watermarking based on integer wavelet
transform-singular value decomposition with variance pixels
Ferda Ernawan1
, Dhani Ariatmanto2
1
Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Malaysia
2
Faculty of Computer Science, Universitas Amikom Yogyakarta, Indonesia
Article Info ABSTRACT
Article history:
Received Aug 9, 2018
Revised Dec 2, 2018
Accepted Dec 30, 2019
With the era of rapid technology in multimedia, the copyright protection is
very important to preserve an ownership of multimedia data. This paper
proposes an image watermarking scheme based on Integer Wavelet
Transform (IWT) and Singular Value Decomposition (SVD). The binary
watermark is scrambled by Arnold transform before embedding watermark.
Embedding locations are determined by using variance pixels. Selected
blocks with the lowest variance pixels are transformed by IWT, thus the LL
sub-band of 8×8 IWT is computed by using SVD. The orthogonal U matrix
component of U3,1 and U4,1 are modified using certain rules by considering
the watermark bits and an optimal threshold. This research reveals an optimal
threshold value based on the trade-off between robustness and
imperceptibility of watermarked image. In order to measure the
watermarking performance, the proposed scheme is tested under various
attacks. The experimental results indicate that our scheme achieves higher
robustness than other scheme under different types of attack.
Keywords:
Embedding technique
Image watermarking
Integer wavelet transform
Singular value decomposition
Variance pixels
Copyright © 2019 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Ferda Ernawan,
Faculty of Computer Systems & Software Engineering,
Universiti Malaysia Pahang,
Lebuhraya Tun Razak 26300 Gambang, Kuantan Pahang Darul Makmur, Malaysia.
Email: ferda@ump.edu.my
1. INTRODUCTION
With rapid growth of internet technology, copyright and security are the major issues towards
multimedia distribution. Digital watermarking technique is an alternative solution to provide multimedia
protection. Image watermarking technique embeds the copyright information which maintains the quality of
the host image. Image watermarking can be performed in spatial and frequency domains. In spatial domain,
the watermark is direcly embedded into the image pixels. This scheme provides low computational cost and
it is easy to be implemented [1]. This technique can achieve high imperceptibility of the watermarked image.
However, this scheme provides weak robustness against noise attacks and JPEG compression.
Image watermarking based on transformed-domain produces a good robustness against several attacks.
The transformed-domains such as Discrete Cosine Transform (DCT) [2]-[5], Discrete Wavelet Transform
(DWT) [6], [7], Integer Wavelet Transform (IWT) [8] have been widely used in image watermarking
schemes.
Most of watermarking schemes applied hybrid scheme between SVD and transform domains to
improve robustness of the watermarked image. watermarking schemes. Lai [9] presented DCT-SVD
watermarking scheme based on human visual characteristics. Lai presented the embedding watermark
technique by examining the first column of orthogonal U matrix. Lai’s scheme can achieve a good
imperceptibility of the watermarked image. However, Lai’s scheme provides weak robustness against speckle
and Gaussian noises. Makbol et al. [10] presented DWT-SVD watermarking technique that consider the

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Int J Elec & Comp Eng, Vol. 9, No. 3, June 2019 : 2185 - 2195
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human visual characteristic. The embedding process was examined orthogonal U matrix of DWT-SVD.
Their scheme has adopted Lai’s scheme for the embedding watermark. Makbol’s scheme achieves higher
robustness and imperceptibility respectively than Lai's scheme. While, DWT filter performs the
transformation in the floating point. It may loose perfect reconstruction during the conversion from integer
image pixels to floating point and from floating point to integer image pixels [11]. It leads round-off errors
that give effect to the quality of the watermarked image.
This paper proposes image watermarking based on IWT-SVD with variance pixels. The embedding
locations are determined based on the variance pixels on each block. Blocks with the lowest variance pixels
are selected for embedded watermark. The number of selected blocks are similar to the number of watermark
bits. The binary watermark bits are scrambled before embedding in order to provide additional security.
Each selected block is transformed using 8×8 IWT, then LL sub-band is computed using SVD.
The orthogonal U matrix of IWT-SVD is modified using some rules with an optimal threshold. The proposed
watermarking scheme may improve the imperceptibility and robustness performance compare to other
schemes. The rest of this paper is organized as follows. The brief preliminaries of variance pixels, Arnold
transform, IWT and SVD are discussed in Section 2. The proposed embedding and extracting watermark are
presented in Section 3. The experimental setup of the proposed watermarking scheme is discussed in Section
4. Section 5 presents the experimental results. Finally, Section 6 concludes the contribution of this paper.
2. PRELIMINARIES
2.1. Variance image pixels
The variance function has been implemented in [12], it has been used to determine the selected
blocks of embedding watermark. The variance pixels indicate the most complex blocks of the image. Its
locations are suitable for embedding watermark in order to improve the invisibility of the watermarked
image. The variance pixels are defined by:
 
1
1
2
2




n
VVi
S
n
i
(1)
where

n represents the number of image pixels on each block,

Vi denotes each pixel and

V represents the
average pixel value on each image block.
2.2. Arnold transform
Arnold transform is performed using modulo operation [13]. This technique changes the pixel
position in order to scramble the binary watermark. The period the scramble watermark is used as a secret
key. Attacker will difficult to identify the information of the embedded watermark even the attacker
successful to extract the watermark. Arnold transform can be defined by:
N
y
x
y
x
mod
21
11
'
'


















(2)
where 





'
'
y
x
represents the vector position after shifting, 





y
x
denotes the original vector position before
shifting and mod denotes the modulus operation after division with N. The inverse Arnold transformation is
defined as:
N
y
x
y
x
mod
'
'
11
12




















(3)
2.3. Integer wavelet transform
Integer wavelet transform uses a lifting scheme to perform the integer to integer wavelet
transform [11]. This technique is used to avoid rounding off errors during the conversion from image pixels
to the transformed coefficients. The lifting scheme may produce perfect reconstruction image watermarking.
The lifting wavelet transform can be done in three stages e.g. split, predict and update. The block diagram of
the lifting and inverse lifting operation is shown in Figure 1.

Int J Elec & Comp Eng ISSN: 2088-8708 
Image watermarking based on integer wavelet transform-singular value decomposition… (Ferda Ernawan)
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Figure 1. Lifting and inverse lifting operations
First stage is split, the signals are divided into even fe and odd fo values. The next stage is predict,
the odd sequence values are predicted with the even sequence in the predictor. Third stage is update
operation, a new even values are obtained by merging the predicted odd value and original even value based
on updater. The predicted odd value represents high frequency coefficients and even value as low frequency
coefficients. The inverse lifting operation can be done by merge operation.
2.4. Singular value decomposition
Block image with the size of N×N can be decomposed using singular value decomposition. The
SVD of an image A can be defined by [14]:
T
USVA  (4)
where U and V represent the orthogonal matrices and S is the diagonal matrix that contain non-negative
value.
3. PROPOSED SCHEME
This section discuss the proposed watermarking scheme based on IWT-SVD with variance pixels.
The embeding and extracting procedures are discussed in the next sub-sections.
3.1. Embedding procedure
In this procedure, the selected regions are determined by lowest variance pixel values of each block.
Each selected block is transformed by IWT-SVD. The U3,1 and U4,1 coefficients of orthogonal U matrix are
modified for embedding watermark. The watermark embedding scheme is illustrated in Figure 2.
The proposed embedding algorithm is presented in Algorithm 1.
Figure 2. Block diagram of embedding watermark
f
fo
fe
Split
+
-
Predict Update
Hi
Lo
f”
fe
fo
Merger
-
+
PredictUpdate

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Algorithm 1. Embedding Algorithms
Input: Host image; watermark;
Pre-processing:
Step 1: An image is divided into non-overlapping blocks of 8×8 pixels
Step 2: The variance of each block is calculated, then the blocks with the lowest variance value are
selected for embedding watermark. The selected blocks are equal to the number of
watermark bits.
Step 3: Save x and y coordinates of each selected block with the lowest variance value.
Step 4: The binary watermark is scrambled using Arnold transform.
Embedding watermark:
Step 5: Each selected block is transformed using 8×8 IWT. Thus, LL sub-band is transformed by
SVD to generate orthogonal U , S , and V matrices.
Step 6: Calculate the average absolute value of component matrix U3,1 and U4,1 coefficient:
b = (|U3,1|+|U4,1|)/2 (5)
Embed each binary watermark bit by modifying orthogonal U matrix with rules:
Rule 1: If Uk,1 is a negative value, set a = -1 and µ = -R else a = 1 and µ = R, for k = 3, 4 and
R represents the optimal parameter trade-off between robustness and
imperceptibility. The optimal threshold R is discussed in detail as presented in
Section 3.3.
Rule 2: If the binary watermark bit = 1, then update Uk,1 by the formula:
Uk,1 = a*b + (-1)k
µ/2 (6)
Rule 3: If the binary watermark bit = 0, then update Uk,1 by the formula
Uk,1 = a*b - (-1)k
µ/2 (7)
where µ is a threshold value of the optimal parameter R, either is a negative or positive
value. If Uk,1 is negative, then we set a and µ as negative and vice versa.
Post-processing:
Step 7: The modified selected blocks are performed by inverse SVD, then we implement inverse
IWT.
Step 8: Combine all the modified selected blocks to generate the watermarked image.
Output: Watermarked image
3.2. Extracting procedure
The block diagram of watermark extraction is illustrated in Figure 3. Step-by-step algorithm of the
extracted watermark is discussed in Algorithm 2. The binary watermark image can be extracted by measuring
the different absolute value of U3,1 and U4,1 coefficients that obtained from IWT-SVD. The extracted
watermark is recovered by applying inverse Arnold transform.
Figure 3. Watermark extraction

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Algorithm 2. Extraction Algorithms
Input: Watermarked image; x and y coordinates of each selected block;
Pre-processing:
Step 1: x and y coordinates are used to determine the embedded regions. The selected regions are divided into non-
overlapping of 8×8 pixels.
Step 2: Apply 8×8 IWT for each selected block.
Step 3: Apply SVD on the first level of LL sub-band of IWT coefficients.
Watermark extraction:
Step 4: Calculate the different absolute value of U3,1 and U4,1 coefficients from orthogonal U matrix of IWT-SVD on each
selected block. If its different value is greater than 0, the extracted watermark bit =1 and vice versa.
Post-processing:
Step 5: The extracted watermark bits are computed by inverse Arnold transform to restore the watermark image.
Output: Recovered Watermark
3.3. An optimal threshold
A threshold value or weight of embedding watermark give a significant effect to imperceptibility
and robustness of the watermarking scheme. A large threshold can achieve high robustness, while at the same
time it produces large distortion and vice versa. An optimal threshold based on a trade-off between
robustness and imperceptibility is important to give a balance for maintaining image quality and resistant
against several attacks. Lai’s scheme [9] and Makbol’s scheme [10] presented various thresholds; e.g. 0.02,
0.012, and 0.04. These thresholds are in tend to produce more robustness or imperceptibility based on the
amount of threshold value. They did not sufficiently consider the optimal threshold value as a trade-off
between imperceptibility and robustness. Therefore, based on these issues, we perform experiments to find
the optimal trade-off between robustness and imperceptibility for the Lai's scheme [9], Makbol’s scheme [10]
and our scheme against JPEG compression. JPEG compression has been widely used in the most of digital
applications [15]-[24] and it is a standard attack to measure the image watermarking performance [25]-[28].
Figure 4 shows a trade-off between NC and SSIM values for Lai’s scheme [9], Makbol’s scheme
[10] and our scheme against JPEG compression. In order to generate the optimal threshold, the trade-off
value is used as a parameter threshold R. The parameter threshold R is incremented with the rate of 0.001.
The threshold for Lai’s scheme [9] is 0.016, Makbol’s scheme [10] is around 0.027 and our proposed scheme
is 0.017. The parameter threshold R is set as the optimal threshold for embedding watermark.
(a) (b)
Figure 4. Trade-off between SSIM and NC values: (a) the Lai’s scheme [9], (b) Makbol’s scheme [10]
0.008 0.01 0.012 0.014 0.016 0.018 0.02 0.022 0.024
0.75
0.8
0.85
0.9
0.95
Threshold against JPEG Compression
Value
Results obtained from JPEG Images
SSIM
NC
0.015 0.02 0.025 0.03 0.035
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
Threshold against JPEG Compression
Value
Results obtained from JPEG Images
SSIM
NC

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(c)
Figure 4. Trade-off between SSIM and NC values: (c) the proposed scheme
4. EXPERIMENTAL SETUP
In this research, we test the proposed scheme in the seven grayscale images with the size of
512×512 pixels. The experiments use a binary watermark image with the size of 32×32 pixels. Seven host
images and a watermark logo are shown in Figure 5.
(a) (b) (c) (d)
(e) (f) (g) (h)
Figure 5. (a) Lena, (b) Pepper, (c) Baboon, (d) Sailboat, (e) Lake, (f) Car, (g) Airplane, (h) watermark image
4.1. Imperceptibility measurement
The perceptual quality of watermarked image can be measured by Structure SIMilarity (SSIM)
index. SSIM is defined by:
1 2
2 2 2 2
1 2
(2 )(2 c )
(x,y)
( )( c )
xy
x y
xy c
SSIM
x y c

 
 

   
(8)

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where x and y represent the image pixels in the host and embedded images, respectively. Two constants
 
2
1 1c k L and  
2
2 2c k L are taken to stabilize the division with weak denominator, where L is the dynamic
range of a pixels, which is 255 for 8 bits per pixel and k1 = 0.01 and k2 = 0.03.
4.2. Robustness measurement
The robustness performance can be measured by Normalized Cross-Correlation (NC) and Bit Error
Rate (BER). NC and BER values are measured after watermarked images are tested under different types of
attack. NC and BER are defined by:
 

   

 


M
i
N
j
M
i
N
j
M
i
N
j
jiWjiW
jiWjiW
NC
1 1 1 1
22
1 1
),(),(
),().,( (9)
NM
jiWjiW
BER
M
i
N
j



 

1 1
),(),(
(10)
where W *(i , j) is the extracted watermark and W(i, j) is the original watermark. M and N denote the row and
column sizes. Several attacks are applied to the proposed watermarking scheme as listed in Table 1. Tabel 1
shows the attack’s abbreviation and the attack’s description. These attacks are also implemented to the other
watermarking schemes.
Table 1. Abbreviation of the Attacks for the Robustness Analysis
Abbreviation Attack's description Abbreviation Attack's description
SPN Salt and Pepper Noise, density = 0.001 HE Histogram Equalization
GN Gaussian Noise, density = 0.0005 JPEG50 JPEG with quality factor = 50
GLF Gaussian Low pass Filter [5 5] σ = 1.2 JPEG70 JPEG with quality factor = 70
SN Speckle noise, density = 0.005 JPEG90 JPEG with quality factor = 90
MF Median Filter [3 3] JPEG2000-9 JPEG2000 with compression ratio = 9
AF Average Filter [3 3] JPEG2000-15 JPEG2000 with compression ratio = 15
SH Sharpening JPEG2000-21 JPEG2000 with compression ratio = 21
CR Cropping row off 25%
5. EXPERIMENTAL RESULTS
This section presents the experimental results of the proposed watermarking scheme and the
comparison to other schemes. The SSIM results obtained from Lai’s scheme, Makbol’s scheme and the
proposed scheme are shown in Figure 6.
Figure 6. Comparison of SSIM values for Lai’s scheme, Makbol’s scheme and proposed scheme
from different watermarked images

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Figure 6 shows the SSIM values of the proposed scheme for seven images. It is noted that our
scheme can achieve higher SSIM values than the other scheme for Baboon watermarked image. Furthermore,
the rest images present slightly lower SSIM value than Lai’s scheme. However, the other scheme is worse
than the proposed scheme. The robustness comparison of Lai’s scheme [9], Makbol’s scheme [10],
Zhang et al.’s scheme [29] and proposed scheme is shown in Table 2.
Table 2 shows the NC values between original watermark and extracted watermark after performing
various attacks on the watermarked image. As can be seen, our scheme achieves high NC values against
speckle noise, Gaussian noise, average filter and compression, which mean high robustness. Based on our
observation, Makbol’s scheme produces higher NC value against salt & pepper noise, sharpening and median
filter than other schemes. While, the other schemes are worse than our scheme. The comparison of NC values
under noise additions, image filters, cropping and compression attacks is shown in Figure 7.
Reffering to Figure 6, it is clear that our scheme shows better performance in terms of NC values
than Lai’s scheme and Makbol’s scheme. It is noted that our scheme is more resistant against noise additions,
image filters and compression attacks. The visual watermark extractions of the Lai’s scheme, Makbol’s
scheme and proposed scheme are shown in Figure 4. It can be observed that the proposed scheme produces
higher quality of the extracted watermark under severe JPEG2000 compression than the existing schemes.
As expected, our scheme has clearly visual quality of the extracted watermark after noise addition. However,
as can be seen from Table 3, Lai’s scheme shows worse distortion of visual extracted watermark against
Gaussian noise and speckle noise. It can be noticed that our scheme also provides distortion of extracted
watermark after median and average filter attacks.
Table 2. NC Values of the Extracted Watermark for Lena Image under Different Types of Attack
Image Processing Attack
Lai [9] Makbol [10] Zhang et al. [29] Proposed
T=0.016 T=0.027 T=0.016 T=0.017
No attack 0.9961 1 1 1
Pepper and Salt Noise 0.001 0.9754 0.9941 0.9923 0.9883
Pepper and Salt Noise 0.005 0.8774 0.9550 0.9566 0.9467
Gaussian Noise 0.0001 0.8444 1 0.9693 0.9990
Gaussian Noise 0.0005 0.6929 0.9772 0.7099 0.9922
Speckle Noise 0.0001 0.9021 1 0.9944 1
Speckle Noise 0.0005 0.8040 1 0.8379 1
Median Filter [3×3] 0.8272 0.9467 0.9386 0.9327
Average Filter [3×3] 0.8223 0.5234 0.8641 0.9285
Image Sharpening 0.8653 1 0.9724 0.9833
JPEG (Q=70) 0.8729 0.9990 0.9793 1
JPEG (Q=80) 0.9452 1 0.9986 1
JPEG (Q=90) 0.9452 1 1 1
Rescaling (2, 0.5) 0.9635 1 1 1
Rescaling (0.5, 2) 0.8252 0.9812 0.9538 0.9068
Cropping (row off 25%) 0.8014 0.8060 0.7828 0.8601
Cropping (Centred 25%) 0.9941 0.9990 0.7083 0.9662
Cropping (column off 25%) 0.8658 0.8703 0.7297 0.8814
Figure 7. Comparison of NC values for Lai’s scheme, Makbol’s scheme and proposed scheme under different
types of attack

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Table 3. Watermark Recovery under Image Processing Attacks
Image Processing
Attack
Lena Pepper
Lai
[9]
Makbol
[10]
Proposed Lai [9]
Makbol
[10]
Proposed
T=0.016 T=0.027 T=0.017 T=0.016 T=0.027 T=0.017
SPN
GN
GLF
SN
MF
AF
SH
CR 1
CR 2
CR 3
HE
JPEG50
JPEG70
JPEG90
JPEG2000-9
JPEG2000-15
JPEG2000-21

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6. CONCLUSION
This paper presents block-based watermarking based on 8×8 IWT-SVD with variance pixels.
The goal of this scheme is to improve the robustness and the imperceptibility of the watermarked image
under different types of attack. The binary watermark image has been embedded into the selected blocks with
lowest variance pixels. The relationship between U3,1 and U4,1 of orthogonal U matrix of 8×8 IWT-SVD was
examined for embedding watermark bits. The watermark bits are not directly embedded into U3,1 and U4,1,
while these coefficients are modified with certain rule conditions by considering watermark bits.
The experimental results show that the proposed scheme produces a good imperceptibility around of 0.98.
Our scheme is tested under image-processing and geometrical attacks e.g., noise additions, image filters and
compression, scale and crop the images. Our scheme shows greater resistant under noise attacks and severe
JPEG2000 compression than other watermarking schemes.
ACKNOWLEDGEMENTS
The authors sincerely thank Universiti Malaysia Pahang, Malaysia for supporting this research work
through UMP Research Grant Scheme (RDU180358).
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Compression,” Advanced Science Letters, vol/issue: 20(1), pp. 70-74, 2014.
[25] F. Ernawan and M. N. Kabir, “A Robust Image Watermarking Technique with an Optimal DCT-Psychovisual
Threshold,” IEEE Access, vol. 6, pp. 20464-20480, 2018.
[26] N. A. Abu, et al., “Image Watermarking using Psychovisual Threshold over the Edge,” Information and
Communication Technology, vol. 7804, pp. 519-527, 2013.
[27] F. Ernawan, “Robust Image Watermarking Based on Psychovisual Threshold,” Journal of ICT Research and
Applications, vol/issue: 10(3), pp. 228-242, 2016.
[28] F. Ernawan, et al., “Block-based Tchebichef Image Watermarking Scheme using Psychovisual Threshold,”
International Conference on Science and Technology-Computer (ICST), pp. 6-10, 2016.
[29] Zhang H., et al., “A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain,” Future Internet,
vol/issue: 9(45), pp. 1-16, 2017.
BIOGRAPHIES OF AUTHORS
F. Ernawan is currently a Senior Lecturer at the Faculty of Computer Systems & Software
Engineering, Universiti Malaysia Pahang. He received his Master in Software Engineering and
Intelligence and Ph.D in image processing from Faculty of Information and Communication
Technology, Universiti Teknikal Malaysia Melaka in 2011 and 2014 respectively. His research
interests include image compression, digital watermarking and steganography (Scopus ID:
53663438800).
D. Ariatmanto is a Lecturer at Faculty of Informatics, University of AMIKOM Yogyakarta.
He received the master's degree in Informatic Engineering from University of AMIKOM
Yogyakarta. He is currently pursuing doctoral program at Universiti Malaysia Pahang. His
research interests include image processing, digital watermarking and multimedia application.

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