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International Journal of Signal Processing, Image Processing and Patter
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Blind Wavelet-Based Image Watermarking
Hanaa A. Abdallah*, Mohiy M. Hadhoud
#
,
Abdalhameed A. Shaalan
*
and Fathi E. Abd El-samie**
*Faculty of Engineering, Zagazig university, Zagazig, Egypt.
#
Faculty of Computers and Information, Menoufia University, Shebin Elkom, Egypt.
**Department of Electronics and Electrical Communications,
Faculty of Electronic Engineering
Menoufia University, Menouf, 32952, Egypt.
E-mails: flower002a@yahoo.com, mmhadhoud@yahoo.com,
dr_shaalan2005@yahoo.com, fathi_sayed@yahoo.com
Abstract
In this paper, a wavelet-based scheme for digital image watermarking is presented.
This proposed scheme is blind, which means that it requires neither the original image nor
any side information in watermark recovery. It is based on inserting the watermark bits into
the coarsest scale wavelet coefficients. Three-level wavelet decomposition and a watermark
equal in size to the detail sub-bands in the coarsest scale are used. Only, perceptually
significant wavelet coefficients are used to embed the watermark bits. The proposed scheme
differs from the traditional wavelet-based schemes in the use of quantization and non-additive
watermark embedding. It produces watermarked images with less degradation than the
traditional wavelet-based schemes.
Keywords: Image watermarking, Wavelet transform, quantization.
1. Introduction
Digital image watermarking has attracted the attention of several researchers in the
last decades. The motivation behind the work in this area is the desire to achieve information
security, information hiding, authentication, and fingerprinting. Several approaches have been
proposed for digital image watermarking. One of such approaches is the discrete wavelet
transform (DWT) approach. The DWT finds a great popularity in the field of watermarking as
it is able to decompose the available images into sub-bands , in which watermarks can be
embedded, selectively [1,2].
Taking the cue from spread spectrum communication, binary watermark data can be
embedded in the wavelet coefficients chosen in a random order. For extraction of the hidden
data, the random sequence must be made available to the extractor. Cox et al. were the first to
apply the spread spectrum technique to data hiding [3]. Were the first to apply the spread
spectrum technique to data hiding Transform domain used DCT and DWT has been used in
[4]. Use of DWT has advantages of speed and robustness against wavelet based compression
[5].
Dugad et al. presented a blind additive watermarking scheme operating in the wavelet
domain [1]. A three-levels wavelet decomposition with Daubechies 8-tap filters was used. No
watermark was inserted into the low-pass sub-band. Unlike some non-blind watermarking
schemes [6,7], this scheme allows a watermark to be detected without access to the original
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image.It performs an implicit visual masking
as only wavelet coefficients with large magnitude are selected for watermark insertion. These
coefficients correspond to regions of texture and edges in an image. This scheme makes it
difficult for a human viewer to perceive any degradation in the watermarked image. Also,
because wavelet coefficients of large magnitude are perceptually significant, it is difficult to
remove the watermark without severely distorting the watermarked image. The most novel
aspect of this scheme was the introduction of a watermark consisting of pseudorandom real
numbers. Since watermark detection typically consists of a process of correlation estimation,
in which the watermark coefficients are placed in the image, changes in the location of the
watermarked coefficients are unacceptable. The watermarking scheme proposed by Dugad et
al. is based on adding the watermark in selected coefficients with significant energy in the
transform domain in order to ensure the non-erasability of the watermark. This scheme has
overcome the problem of “order sensitivity”.
Unfortunately, this scheme has also some disadvantages. It embeds the watermark in
an additive fashion. It is known that blind detectors for additive watermarking schemes must
correlate the possibly watermarked image coefficients with the known watermark in order to
determine if the image has or has not been marked. Thus, the image itself must be treated as
noise, which makes the detection of the watermark exceedingly difficult [8]. In order to
overcome this problem, it is necessary to correlate a very large number of coefficients, which
in turn requires the watermark to be embedded into several image coefficients at the insertion
stage. As a result, the degradation in the watermarked image increases. Another drawback is
that the detector can only tell if the watermark is present or not. It cannot recover the actual
watermark.
The scheme in [9] is another example of wavelet-based watermarking schemes. A
noise-like Gaussian sequence is used as a watermark. To embed the watermark robustly and
imperceptibly, watermark components are added to the significant coefficients of each
selected sub-band by considering the human visual system (HVS) characteristics. Some small
modifications are performed to improve the HVS model. The host image is needed in the
watermark extraction procedure.
In this paper, we present a new scheme to avoid these drawbacks. It is possible to use
the advantages of the watermarking scheme presented by Dugad, whilst avoiding its
disadvantages. This can be accomplished by using a binary watermark equal in size to the
detail sub-bands in the coarsest wavelet scale in conjunction with an adapted version of the
scalar quantization insertion/detection technique. The proposed watermarking scheme is
blind. Only, perceptually significant coefficients are used to embed the watermark bits. The
proposed scheme is expected to produce watermarked images with less degradation than the
Dugad’s scheme.
This paper is organized as follows. Sections 2 and 3 introduce two traditional
wavelet-based watermarking schemes. Section 4 introduces the proposed watermarking
scheme. Section 5 introduces the perceptual quality metrics that will be used for the
assessment of watermarking schemes. Section 6 introduces the experimental results. Finally,
section 7 gives the concluding remarks.
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2. Dugad’s scheme
Dugad et al. presented an additive watermarking scheme operating in the wavelet
domain [1]. The steps of watermark embedding and detection in this scheme are summarized
in the following subsections
.
2.1. Embedding algorithm
The steps of watermark embedding in Dugad’s scheme can be summarized as follows:
1. Wavelet decomposition is performed on the original image. After this decomposition,
we get four components; the approximation (LL
1
) component, the horizontal details (HL
1
)
component, the vertical details (LH
1
) component, and the diagonal details (HH
1
)
component.
2. A random watermark matrix of zero mean and unit variance, which is equal in size to
the detail components of the input image, is generated with a known seed value.
3. All wavelet coefficients in the HL
1
and LH
1
components with magnitude greater than
a threshold t
1
are selected. This ensures that only perceptually significant coefficients are
used.
4. The watermarking is performed on the wavelet coefficients selected in step 3 as
follows[1]:
ˆ
ij ij ij ij
w w w x
α
= +
(1)
where w
ij
is a selected wavelet coefficient at indices (i,j),
α
is a scaling parameter, x
ij
is a
watermark value, and
ˆ
ij
w
is the watermarked wavelet coefficient.
2.2 Detection algorithm
1- The watermark is regenerated using the known seed value.
2- Wavelet decomposition is performed on the possibly corrupted watermarked image.
3- All wavelet coefficients, from all components barring the LL
1
, of magnitude greater than t
2
are selected. Note that by setting t
2
> t
1
, the robustness of the algorithm is increased, as the
magnitude of some wavelet coefficients, which were originally below t
1
, may become greater
than t
1
due to image manipulations.
4- The selected coefficients are correlated with the watermark values at the same locations.
After this correlation process, a yes or no answer will be given for the presence of the
watermark.
3. Miyazaki’s scheme
Two watermarking schemes were presented by Miyazaki et al. in [2]. Both schemes
are implemented in the wavelet domain, but each targets a different set of coefficients for
insertion. The first scheme operates upon insignificant coefficients, whereas the second
scheme operates upon significant coefficients. Thus, both insertion schemes could be applied
to the same image at the same time. However, the reported results indicate that the insertion
technique utilizing the significant coefficients is more robust than the insertion technique
operating utilizing the insignificant coefficients. For this reason, only the insertion technique
utilizing the significant coefficients will be considered in this paper.
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This scheme depends on a three levels wavelet decomposition of the image to be
watermarked and inserts the watermark into the detail coefficients at the coarsest scale. It is a
quantization based scheme, which aims at modifying the wavelet coefficients of high
magnitude, and thus embedding the watermark into the edge and texture regions of the image.
It is a semi-blind scheme as it requires a file containing the locations, where the watermark
was embedded in order for the detector to work.
4. Proposed watermarking scheme
The proposed watermarking scheme is a blind quantization based scheme. A block
diagram detailing its steps is shown in Figure 1.
Figure 1. The proposed image watermarking scheme.
4.1 Watermark Embedding
The steps of watermark embedding can be summarized as follows:
1. The host image is transformed into the wavelet domain; three levels Daubechies wavelet
with filters of length 4 is used.
2. The coefficients in the third wavelet level (excluding the LL
3
and HH
3
sub-bands) with
magnitude greater than t
1
and less than t
2
are selected. Let
max
f
be the wavelet coefficient
DWT
(3 levels)
N
×
N
Input image
HL
3
LH
3
Owner seed
Binary watermark
01010001110………
….
Embedding via
quantization IDWT
Watermarked
image
HL
1
HH
1
LH
1
HH
2
HL
2
LH
2
HH
3
LL
3
HL
3
LH
3
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with maximum absolute in both HL
3
and LH
3
sub-bands. A threshold t= α.
max
f
is
selected, where
0.01 <α <0.1 and t
2
> t
1
>t. (2)
3. A binary watermark of the same size as the two sub-bands of interest is created using a
secret key, which is a seed of a random number generator.
4. Each
s
ij
w
of the selected wavelet coefficients is quantized. The quantization process can
be summarized as follows:
If
ij
x = 1 and
s
ij
w
> 0, then
s
ij
w
'
= t
2
– x
1
,
If
ij
x = 0 and
s
ij
w
> 0, then
s
ij
w
'
= t
1
+ x
1
,
If
ij
x= 1 and
s
ij
w
< 0, then
s
ij
w
'
= -t
2
+ x
1
,
If
ij
x= 0 and
s
ij
w
< 0, then
s
ij
w
'
= -t
1
- x
1
, (3)
where
ij
x is the watermark bit corresponding to
s
ij
w
, and
s
ij
w
'
is the watermarked wavelet
coefficient. The parameter x
1
narrows the range between the two quantization levels t
1
and t
2
in order to perform a robust oblivious detection. Figure (2) shows the watermark embedding
in a positive wavelet coefficient.
5. After all the selected coefficients are quantized, the inverse discrete wavelet transform
(IDWT) is applied and the watermarked image is obtained.
Figure 2. Watermark embedding for positive wavelet coefficients in the
proposed scheme.
4.2. Watermark Detection
1. The possibly corrupted watermarked image is transformed into the wavelet domain using
the same wavelet transform as in the embedding process.
Lowest value
of Wavelet
coefficients
Highest value
of Wavelet
coefficients
Out of
range Out of
range
Within range
t
1
t
2
x
1
x
1
t
1
+ x
1
t
2
- x
1
WM= WM=
s
ij
w
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2. The extraction is performed on the coefficients in the third wavelet level (excluding the
LL
3
and HH
3
sub-bands).
3. All the wavelet coefficients of magnitude greater than or equal to t
1
+ x
2
and less than or
equal to t
2
– x
2
are selected; these are denoted
s
ij
w
'
. Note that x
2
should be less than x
1
.
This helps to ensure that all the marked coefficients are recovered and
dequantized after being attacked. Unmarked coefficients are unlikely to drift into the
range of selected coefficients after an attack. The introduction of the parameters x
1
and x
2
to the watermarking algorithm gives a degree of tolerance to the system against attacks,
i.e., they collaborate to give a noise margin. The watermark bits are extracted from each
of the selected wavelet coefficients with Eq. (4). Figure (3) illustrates the watermark
detection process.
Figure 3. Watermark detection in the proposed scheme.
If
s
ij
w
'
< (t
1
+ t
2
)/2, then the recovered watermark bit is a 0.
If
s
ij
w
'
≥ (t
1
+ t
2
)/2, then the recovered watermark bit is a 1 (4)
4. The recovered watermark is then correlated with the original watermark in the watermark
file, obtained via the secret key, only in the locations of the selected coefficients. This
allows a confidence measure to be ascertained for the presence or absence of a watermark
in an image.
5. Perceptual quality metrics
Two metrics for ascertaining the quality of a watermarked image are highlighted in
this section. These metrics are the Mean Square Error (MSE), and the Peak Signal to Noise
Ratio (PSNR). The MSE measures the average pixel-by-pixel difference between the original
image (I) and the watermarked image (
I
ˆ
) [9].
2
,
,
,
)
ˆ
(
1
nm
nm
nm
II
MN
MSE −= ∑
(5)
MSE
I
dBPSNR
peak
2
10
log10)( =
(6)
where I
peak
is the peak intensity level in the original image (most commonly 255 for an 8-bit
grayscale image), M and N are the dimensions of the image.
3 levels
DWT
Watermarked
image
Extraction of
s
ij
w
'
From HL
3
and LH
3
If
s
ij
w
'
< (t
1
+
t
)/2
If
s
ij
w
'
≥ (t
1
+
WM=0
WM=1
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The limitations of pixel based image quality metrics lead to other quality metrics that
are based on the HVS. Two of such metrics were presented by Lambrecht et al. [10] and
Watson [11]. The Lambrecht metric was described by Kutter et al. as a fair and viable method
for determining the amount of degradation suffered by a watermarked image. It makes use of
coarse image segmentation to examine contrast sensitivity as well as the masking phenomena
of the HVS. This metric then returns an overall measure of the distortion of the watermarked
image compared to the original image. The Watson metric was incorporated into the
Checkmark package [12]. It operates in the DCT domain and utilizes contrast sensitivity,
luminance masking and contrast masking in order to calculate a Total Perceptual Error (TPE)
value between the watermarked and original images.
The original and recovered messages or watermarks can be compared by computing the
Normalized Correlation (NC)[9]:
mm
mm
NC .
.
*
*
=
(7)
where m is the original message and
*
m is the recovered message. For unipolar vectors, m
∈
{0, 1}, and for bipolar vectors, m
∈
{−1, 1}.
6. Simulation Results
This section presents experimental results to compare between the Dugad’s scheme,
Miyazaki’s scheme, and the proposed scheme for image watermarking. Images are
watermarked using the three watermarking schemes and subjected to attacks. In order to
measure the degradation suffered by host images after watermark insertion, the PSNR and the
TPE are used. The higher the TPE value, the more degraded an image would appear to a
human viewer. The Checkmark package is used to determine the TPE value.
For all the tests in this paper, MATLAB is used. All tests are performed upon the 8-
bit grayscale 256 × 256 Mandrill and Hat images. To simulate the watermarking schemes on
the Mandrill image, we set t
1
= 115, t
2
= 200. These thresholds are obtained from Figs. (4-a)
and (4-b) to make a trade-off between the required high PSNR of the watermarked image and
high NC of the extracted watermark in the presence of a resizing attack. Resizing is
performed from size 256 × 256 to 128 × 128 and back to 256 × 256. The thresholds used for
the Hat image watermarking are obtained from Figs. (4-c) and (4-d) as t
1
= 90, t
2
= 200. To
simulate the proposed watermarking scheme, we find f
max
and set α = 0.1 to obtain the value
of T=0.1f
max
. We also take x
1
=10 and x
2
=5. Results of all schemes for the Mandrill and Hat
images are shown in Figs.(5) and (6), respectively. The numerical evaluation metrics for all
schemes in the absence and presence of attacks are tabulated in Tables (1) to (6). From Tables
(1) and (4), we notice that the proposed watermarking scheme achieves the lowest distortion
in the watermarked images in the absence of attacks. From Tables (2) and (5), we notice that
the proposed blind watermarking scheme has a better performance than Miyazaki’s scheme,
which is also blind, for most of the attacks. The Dugad’s scheme gives a better performance
than the both the proposed scheme and Miyazaki’s scheme because it is a non-blind scheme.
In fact, the need to blind watermarking schemes is more urgent than that for non-blind
schemes. From Tables (3) and (6), we notice also that a percentage of around 50% of the
input watermark bits can be extracted in the proposed scheme with most of the attacks.
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(a) (b)
(c) (d)
Figure 4. (a) Variation of the PSNR of the host image with thresholds t
1
and t
2
for the Mandrill image. (b) Variation of the NC between the original watermark
and the extracted watermark with thresholds t
1
and t
2
for the Mandrill image in
the presence of a resizing attack. (c) Variation of the PSNR of the host image
with thresholds t
1
and t
2
for the Hat image. (d) Variation of the NC between the
original watermark and the extracted watermark with thresholds t
1
and t
2
for
the Hat image in the presence of a resizing attack.
(a)
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(b) (c) (d)
Figure 5. (a) Original Mandrill image. (b) Mandrill image marked with Dugad’s
scheme in the absence of attacks. (c) Mandrill image marked with Miyazaki’s
scheme in the absence of attacks. (d) Mandrill image marked with the
proposed scheme in the absence of attacks.
(a)
(b) (c) (d)
Figure 6. (a) Original Hat image. (b) Hat image marked with Dugad’s scheme in
the absence of attacks. (c) Hat image marked with Miyazaki’s scheme in the
absence of attacks. (d) Hat image marked with the proposed scheme in the
absence of attacks.
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Table 1. Evaluation metrics values for all schemes for the Mandrill image.
Scheme PSNR (dB) TPE
Dugad’s scheme (Blind) 42.48 0.01
Miyazaki’s scheme
(Non-blind) 44.65 0.0079
Proposed scheme
(Blind) 46.60 0.007
Table 2. The NC of the extracted watermarks for all schemes for the Mandrill
image.
Dugad’s
scheme
Miyazaki’
s
scheme
Propose
d
Scheme
No
attacks 0.57 1 1
JPEG
Q5 0.21 0.75 0.14
JPEG
Q10 0.22 1 0.48
JPEG
Q15 0.52 1 0.85
Gaussia
n noise 0.53 0.87 0.54
Impulsiv
e noise 0.58 0.95 0.79
Croppin
g 0.11 0.35 0.48
Resizing 0.23 0.75 0.39
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Table 3. The extracted watermark length in the proposed scheme for the
Mandrill image. The input watermark length is 102 bits.
Table 4. Evaluation metrics values for all schemes for the Hat image.
Scheme PSNR TPE
Dugad’s scheme (Blind) 40.09 0.021
Miyazaki’s scheme (Non-
blind) 44.62 0.013
Proposed scheme (Blind) 45.36 0.012
Type of
attack
Extracted
watermark
length
No attacks 102
JPEG Q5 53
JPEG Q10 77
JPEG Q15 79
Gaussian
noise 54
Impulsive
noise 79
Cropping 38
Resizing 48
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Table 5. The NC of the extracted watermarks for all schemes for the Hat image.
Table 6. The extracted watermark length in the proposed scheme for the Hat
image. The input watermark length is 367 bits.
7. Conclusions
This paper presented a blind wavelet-based image watermarking scheme. This
scheme depends on the quantization of certain wavelet coefficients within certain amplitude
ranges in a binary manner to embed meaningful information in the image. Experimental
results have shown the superiority of the proposed scheme from the host image quality point
of view and the blindness point of view.
Dugad’s
scheme
Miyazaki’s
scheme
Proposed
scheme
No attacks 0.45 1
1
JPEG Q5 0.27 0.44 0.28
JPEG Q10 0.38 0.66 0.46
JPEG Q15 0.45 1 0.88
Gaussian
noise 0.37 0.75 0.57
Impulsive
noise 0.42 0.79 0.45
Cropping 0.20 0.32 0.39
Resizing 0.36 0.5 0.49
Type of
attack
Extracted
watermark
length
No attacks 367
JPEG Q5 203
JPEG Q10 271
JPEG Q15 319
Gaussian
noise 250
Impulsive
noise 293
Cropping 78
Resizing 222
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Authors
Hanaa A. Abdallah received the BSc and MSc. degrees from the
faculty of Engineering from zagazig University, Egypt in 1998 and
2002, respectively. She is currently an Assistant Lecturer in the Dept.
of Electronics and Communications engineering, Faculty of
Engineering, zagazig University.She is currently working towards
the Ph.D. degree in Communications Engineering from the zagazig
University. Her areas of interests are image processing, image
enhancement image compression, data hiding,
steganography,watermarking.
Mohiy M. Hadhoud
received the BSc and MSc degrees in Electrical
Engineering from Menoufia University in Egypt in 1976 and 1981
respectively. He received the PhD degree from Southampoton
University in 1987. He is currently the dean of the Faculty of
Computers and Information, Menoufia University. His areas of
interests are signal processing, Image Processing and Digital
Communications
International Journal of Signal Processing, Image Processing and Pattern Recognition
International Journal of Signal Processing, Image Processing and Pattern RecognitionInternational Journal of Signal Processing, Image Processing and Pattern Recognition
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol. 4, No. 1, March 2011
Vol. 4, No. 1, March 2011Vol. 4, No. 1, March 2011
Vol. 4, No. 1, March 2011
28
Abdelhamid A. Shaalan received his MSc in microwave
engineering from Faculty of Engineering, Cairo University, Egypt in
1991. He received his PhD in Microwave Engineering from
Faculty of Engineering, Cairo University in 1996. He is an associate
Professor in communication engineering at Faculty of engineering,
Zagazig University, Egypt. His research interests include antenna
engineering and its applications.
Fathi E. Abd El-Samie received the B.Sc. (Honors), M.Sc., and
PhD. from the Faculty of Electronic Engineering, Menoufia
University, Menouf, Egypt, in 1998, 2001, and 2005, respectively.
He joined the teaching staff of the Department of Electronics and
Electrical Communications, Faculty of Electronic Engineering,
Menoufia University, Menouf, Egypt, in 2005. He is a co-author of
about 130 papers in national and international conference
proceedings and journals. He has received the most cited paper
award from Digital Signal Processing journal for 2008. His current
research areas of interest include image enhancement, image restoration, image interpolation,
superresolution reconstruction of images, data hiding, multimedia communications, medical
image processing, optical signal processing, and digital communications.