Improving Watermark Performance by Using Bipolar Split

國立臺南大學
「理工研究學報」第 卷第 期（民國 ）：
44
1
99.4
83-102
Improving Watermark Performance by Using
Bipolar Split
Jiann-Shu Lee
Department of Computer Science and Information Engineering,
National University of Tainan
Abstract
In this paper, we propose a so-called bipolar split scheme to promote watermarking robustness
while not at the expense of imperceptibility which is desired to be resolved in the research area. The
watermark is split up into bipolar parts such that we can enhance the watermarked image quality by
3dB without loss of the watermark robustness. The experimental study shows our method can gain
about 3dB benefit in PSNR beyond the single watermark with double strength method. And, our
method can even compete in the extracted watermark quality with the latter.
: watermarking, attack simulation, bipolar split.
Keywords
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Improving Watermark Performance by Using Bipolar Split
1. Introduction
The protection of intellectual property rights for digital media has become an important issue
due to the rapid and extensive growth of the Internet, popular digital recording and storage devices
such that digital content can be replicated, transmitted and distributed in an effortless way. In the
decade, digital watermarking proposed to protect the copyright of the legal owners and providers
has become an active research area. A qualified watermark technique should possess several
properties such as robustness, imperceptibility, security and unambiguity. For still image
watermarking, many techniques (Wolfgang, 1999; Voloshynovskiy, 1999; Kutter, 1997; Delaigle,
1998; Cheng, 2001; Barni, 2001; Depovere, 1998; Karybali, 2003; Delaigle, 2002; Pereira, 2001;
Cox, 1997; Swanson, 1996; Podilchuk, 1998; Watson, 1992; Chou, 1995; Lee, 2006; Amornraksa,
2006; Hsieh, 2007; Hsu, 1999; Hsu, 1998) have been proposed. Generally speaking, they can be
divided into spatial domain and frequency domain approaches.
Spatial domain approaches are apt to achieve imperceptibility while sustaining low robustness.
On the contrary, the frequency domain approaches are more robust because the watermark has been
spread to the whole image and hence is resistant to cropping attack. Hence, the lately proposed
watermarking approaches almost belong to this category. In the literature, (Cox, Kilian, Leighton, &
Shamoon, 1997)proposed a global DCT-based approach to hide watermarks. An independent and
identically distributed Gaussian random vector is constructed as a watermark and is inserted in a
spread-spectrum-like fashion into the perceptually most important spectral components of the data.
(Swanson, Zhu, & Tewfik, 1996) integrated the human visual system (HVS) model to watermarks
hiding. For each DCT transformed block, a frequency mask is computed like the low bit rate coding
algorithms. The mask is scaled and multiplied by the DCT of the watermark under imperceptible
constraint. (Podilchuk & Zeng, 1998) proposed two perceptually based watermarking schemes, i.e.
the block-based discrete cosine transform and the wavelet transform. The Watson’s perceptual
model (Watson, 1992) is adopted in both schemes. The watermark values are generated from a
normal distribution with zero mean and unit variance. If the frequency component magnitude is
larger than the corresponding contrast masking threshold (treated as JND), the watermark value is
weighted by the JND (Chou & Li, 1995) (just noticeable differences) and then is added to the
frequency component. (Lee, Tsai, & Ko, 2006) designed the watermark embedding from the attack
perspective. They developed a new JND in the DCT domain based on an attack simulation scheme
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理工研究學報
that reversely derives the maximum modification amount embedded in the DCT domain under a
condition of imperceptibility. (Amornraksa & Janthawongwilai, 2006) proposed new watermarking
methods by balancing the watermark bits around the embedding pixels in the watermark preparing
process, by properly tuning the strength of watermark in the embedding process, and by modifying
the linear combination of nearby pixel values around the embedded pixels in the retrieval process.
(Hsieh & Liao, 2007) designed three corresponding detectors based on three attacks. The detectors
remove the unreliable DCT coefficients in the attacked image and only reliable coefficients are used
for the extraction of the watermark embedded. In (Hsu & Wu, 1999), watermarks are embedded by
modifying the middle-frequency coefficients within each image block of the original image in
considering the effect of quantization to survive the cropping of an image, image enhancement and
the JPEG lossy compression. In (Hsu & Wu, 1998) both the watermark and the host image are
composed of multiresolution representations with different structures and then the decomposed
watermarks of different resolution are embedded into the corresponding resolution of the
decomposed images. This approach can preserve the corresponding low resolution components of
the image in case of image quality degradation.
For image watermarking, how to promote the watermarking robustness while not at the
expense of imperceptibility is an important research issue. In this paper, we try to solve this
problem by using bipolar split. The underlying concept comes from the properties that the quality
degradation of the watermarked image reflects in energy, e.g. the PSNR, while the watermark
robustness is determined by the embedded strength of DCT coefficients (for DCT based
watermarking). Since the DCT is a unitary transformation, we can explain the idea by the following
scenario. Suppose a watermark is embedded by increasing one DCT coefficient magnitude Δ and
results in square error E. If we split the watermark into two ones with magnitudes Δ /2 and - Δ /2
respectively, the total square error will become E/2 while the relative watermark strength is still Δ .
This means that if we split the watermark strength into a bipolar manner, the watermarked image
quality can be enhanced by 3dB not at the cost of watermark robustness. Equivalently, if we retain
the image quality at the original level, the watermark robustness will can be further promoted. For
signal processing attacks, e.g. smoothing and sharpening, the induced disturbances on the DCT
coefficients are locally correlated. Hence, the bipolar watermarks are embedded at neighboring
DCT coefficients to take the advantage from this correlation. The experimental study shows that the
85
Improving Watermark Performance by Using Bipolar Split
bipolar watermarks split approach can gain about 3dB benefit in PSNR beyond the single
watermark with double strength method. And, the former can even compete in the extracted
watermark quality with the latter.
The bipolar split benefit is proven in section 2. Section 3 introduces the attacking simulation
approach. The bipolar watermarks insertion and extraction are addressed in section 4. Section 5
depicts the experimental results. Finally, Section 6 gives the concluding remark of this paper.
2. The advantage of bipolar split
Let f and F be a 2-D image with M’s rows and N’s columns and its corresponding 2-D discrete
cosine transform, respectively. Their corresponding DCT pairs are shown in the following.
F (u v ) =
,
and
f (i, j ) =
4
MN
M −1 N −1
fi j
∑∑
i j
C (u )C (v )
(
=0
,
cucvFu v
∑∑
u v
(
,
) cos
=0 =0
where C (u ) =
2
if u
2
i + )uπ
M
(2
1
2
=0
M −1 N −1
( ) ( )
) cos
cos
(2
j + )vπ
N
1
2
(2 j + 1)vπ
i + 1)uπ
cos
2M
2N
(2
(1)
(2)
= 0, otherwise C (u ) = 1 . Suppose a watermark coefficient w(p, q) is
inserted into the DCT coefficient F(m, n), where m*n≠0, by adding Δ to it. The modified F is
denoted as F d and its corresponding image is written as f d . Now, we can calculate the PSNR of
the distorted image, i.e. PSNR( f d ) = 10log10 (2552/ v d ).
vd =
1
N
2
∑∑ f i j − f
N −1 N −1
i =0 j =0
∑∑
( , )
d
i j
( , )
Δ2 N −1 N −1 (2i + 1)mπ
cos
N 2 i =0 j =0
2N
2
Δ
= 2
4N
=
2
cos
(2
j + 1)nπ
2N
2
(3)
On the contrary, suppose we insert the watermark w(p, q) by adding Δ /2 to F(m, n) and
subtracting Δ /2 from F(r, s). The modified F is denoted as F s and its corresponding image is
written as f . We can calculate the PSNR of the distorted image, i.e. PSNR( f ) =
s
s
10log10(2552/ v ).
s
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理工研究學報
vs =
N
∑∑ f i j − f
N −1 N −1
1
2
Δ
= 2
4N
Δ2
= 2
8N
2
( , )
i =0 j =0
∑∑
N −1 N −1
i =0 j =0
cos
s
i j
( , )
i + 1)mπ
2N
(2
Hence, we can find that
f
2
cos
s
(2
(2i + 1)rπ
j + 1)nπ
+ cos
2N
2N
improves the PSNR of the
cos
f
d
(2
j + 1)sπ
2N
2
(4)
by 3dB. This proves that the
bipolar split approach indeed can enhance the watermarked image quality. Because the relative
strength of the both modified DCT coefficients of F is equal to the strength of the modified
coefficient of F d , the effective robustness of the both strategies are equivalent from the theoretical
view.
This bipolar split scheme can be combined with the existing watermark insertion and
extraction approaches to achieve an effective watermarking system. Here, for demonstrating the
effectiveness of this scheme, we take the attacking simulation method (Barni, Bartolini, De Rosa, &
Piva, 2001) as an instance.
s
3. The attacking simulation approach
This method takes the positive and the negative attacks to the original image to derive the
acceptable modification amount for each DCT coefficient. Based on the constraint that the image
quality degradation of the factitiously attacked image must be imperceptible, the spatial JND model
is applied to limit the modification amount.
The flow chart of the DCT domain JND computing is shown in Fig.1. The factitiously negative
attack and positive attack are described as follows. For factitiously negative attack, the image is first
−−
divided into eight by eight non-overlapping blocks. For each block, the average intensity P and
every pixel’s JND value JND( x, y) are calculated. Then, each pixel in every block is adjusted
−−
toward P just like contrast suppression to simulate the negative attacks. For each pixel, the
maximum adjustment amount is less than JND( x, y) and the adjusted result cannot cross the
−−
average intensity P . Let P ( x, y ) and JND ( x, y) denote the intensity value and the JND value
j
j
of the pixel at location (x, y) in the block j, respectively. The average intensity of the block j is
−−
represented as P . And, let P − ( x, y) be the adjusted result after the factitiously negative attack.
j
j
We can express the process by equations (5) to (7):
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Improving Watermark Performance by Using Bipolar Split
if P ( x, y ) > P
otherwise
⎩
A = P ( x, y ) + B − * JND ( x, y )
⎧− 1,
B− = ⎨
1,
j
(5)
j
j
(6)
j
⎧max(A, P ),
P − ( x, y) = ⎪⎨
if P ( x, y) > P
otherwise
j
j
⎪
⎩ min(A, P ),
j
j
(7)
j
Similarly, the factitiously positive attack is achieved by adjusting each pixel value P ( x, y )
−−
backward the average intensity P just like the contrast enhancement. Let P + ( x, y) be the
j
adjusted result in the block j after the factitiously positive attack. This process can be expressed
explicitly by equations (8) to (10):
if P ( x, y ) > P
otherwise
j
(8)
A = P ( x, y ) + B + * JND ( x, y )
(9)
⎧ 1,
B+ = ⎨
⎩− 1,
j
j
j
⎧min(A,255),
if P ( x, y) > P
otherwise
P + ( x, y) = ⎨
j
⎩max(A,0),
j
(10)
j
Meanwhile, the additional constraints, i.e. P + ( x, y ) − P ( x, y ) ≤ JND ( x, y ) and
P − ( x, y) − P ( x, y) ≤ JND ( x, y) are added to the both factitious attacks respectively to ensure
j
j
j
j
j
j
that the modified result is imperceptible to human eyes.
We derive the DCT domain JND by computing the difference between the original image’s
DCT and the attacked image’s DCT. The DCT coefficients of the original image and the factitiously
positive and negative attacked images are calculated block by block. And, the corresponding DCT
coefficients of the jth blocks are denoted as c (u, v) , c + (u, v) , and c − (u, v) , accordingly. We
j
j
j
calculate the absolute differences of these coefficients and let
+
+
dctjnd (u, v) = | c (u, v) - c (u , v ) |
j
j
(11)
j
and
−
= | c − (u, v) - c
Then, the DCT domain JND
dctjnd (u, v)
j
and
j
j
(u , v )
|.
(12)
dctjnd (u, v)
j
−
dctjnd (u, v) .
j
88
is defined as the minimum of
dctjnd
+
j
(u , v)
理工研究學報
4. Inserting and extracting the watermark
4.1 Inserting method
Let be the host image with size x , and the digital watermark be a binary image
with size x . In the watermark, the marked pixels are valued as one’s, and the others are
zeros. There are , i.e. /8 x /8, image blocks with size 8 x 8 in the host image . To obtain
the same number of blocks as the image , the watermark is decomposed into blocks with size
( x 8/ ) x ( x 8/ ). The inserting algorithm is expressed as follows.
Step 1 :The host image is divided into 8×8 non-overlapping blocks. To survive picture cropping,
the torus automorphism algorithm is used to permute the watermark to disperse its spatial
relationship. After permutation, the th watermark in the block is denoted as ( ).
Step 2 : Each block in the host image is
transformed independently.
Step 3 : Computing the
domain
as described in the section 3.
Step 4 : For each image block, there are ( x 8/ ) x ( x 8/ ) watermark coefficients and
( x 8/ ) x ( x 8/ ) complementary watermark coefficients needed to be
embedded. The former are called master watermarks and the latter are called slave
watermarks. We choose the position ( , ) of the largest
( , ) from those unvisited
positions to embed the master watermark in the block . And, then these positions are
labeled as visited. If ( ) = 1, the
coefficient c (u, v) is updated by using Eq. (13)
I
N1
N2
W
M2
M1
NI
N1
N2
I
I
M1
N1
M2
W
N2
I
i
j
wj i
DCT
DCT
JND
M1
M1
N1
M2
N1
M2
N2
N2
u
v
dctjndj u
v
j
wj i
where 1 ≤ ≤ ( x 8/ ) x (
to modify c (u, v) .
i
i
M1
N1
DCT
j
x 8/ ). On the contrary, if ( ) = 0, Eq. (14) is applied
M2
N2
wj i
j
c (u, v) = c (u, v) + dctjnd (u, v)
(13)
c (u, v) = c (u, v) − dctjnd (u, v)
(14)
*
j
j
j
*
j
j
j
Step 5 : For the slave watermark insertion, we select the position ( , ) from the 3*3
neighborhood of the master watermark, which possesses the same sign with c (u, v) . If
( ) = 1, the
coefficient c (u, v) is updated by using Eq. (14). On the contrary, if
( ) = 0, the equation (13) is applied to modify c (u, v) .
y
z
j
wj i
DCT
j
wj i
Step 6 : Take the inverse
j
DCT
of the modified result to obtain the embedded image.
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Improving Watermark Performance by Using Bipolar Split
4.2 Extracting method
The watermark extraction can be modeled as the hypothesis testing problem concerning
two-valued observations and :
M
S
H : ( M , S ) = (ε M , ε S ) + ( μ M
0
versus
H : ( M , S ) = (ε M , ε S ) + ( μ M
1
,0
, μ S ,0 )
(15)
,1
, μ S ,1 )
.
Here, we assume that the noises ε M and ε S are Gaussian random variables with zero
means. In terms of distributions on the observation space, the hypothesis pair of (15) can be
rewritten as
H : ( M , S ) ~ N ( μ M , μ S , Σ)
0
,0
,0
versus
H : ( M , S ) ~ N ( μ M , μ S , Σ)
1
,1
(16)
,1
where N ( μ M i , μ S i , Σ) denotes two-dimensional Gaussian distribution with means μ M i
and μ S i and covariance matrix Σ . The likelihood ratio for (16) is given by
,
,
,
,
L(m, s) =
p1 (m, s)
p0 (m, s)
−
Σ
1/ 2
=
π
2
−1 / 2
Σ
exp[
π
2
= exp[
1
2
−
1
−
1
exp[
(
2
2
(
(
X − C ) Σ − ( X − C )]
t
1
1
1
X − C ) Σ− ( X − C
t
1
0
0
)]
X − C0 ) Σ −1 ( X − C0 ) − 1 ( X − C1 ) Σ −1 ( X − C1 )]
t
t
2
90
(17)
理工研究學報
where X
⎡m⎤
= ⎢ ⎥,
⎣s⎦
C
⎡μm ⎤
=⎢
⎥,
⎣ μs ⎦
,0
0
C
,1
1
,0
⎡μm ⎤
=⎢
⎥,
⎣ μs ⎦
L is the likelihood ratio and p0 and p1 are
,1
two probability distributions corresponding to H0 and H1, respectively. Thus a Bayes test for (16)
is given by
⎧
1
⎪
⎪
δ B ( m, s ) = ⎨
⎪0
⎪
⎩
where
τ
=
if
exp[
if
exp[
1
2
1
2
(
X − C0 )t Σ −1 ( X − C0 ) −
1
(
X − C0 ) Σ ( X − C 0 ) −
1
t
−1
2
2
(
X − C1 )t Σ −1 ( X − C1 )]≥ τ
(
X − C1 ) Σ ( X − C1 )]< τ
t
−1
(18)
π 0 (C10 − C00 )
, π is the prior probability of the occurrence of hypothesis
π 1 (C01 − C11 )
i
and C is the cost incurred by choosing hypothesis H when hypothesis
ij
i
case, we let C00 = C11 = 0 and C01 = C10 = 1 . The ratio
H
i
H is true. In our
j
π0
is equal to the ratio of the white
π1
pixel number to the black pixel number of the embedded watermark. To get explicit threshold
value for the master and slave watermark pair some assumptions should be made. We assume that
the probability distributions of ε m and ε are independent and the variances of ε m and ε
s
s
are the same and equal to σ . Then, the watermark detection rule can be rewritten as
⎧
⎪1
⎪
δ B ( m, s ) = ⎨
⎪0
⎪⎩
if
m − s≥
if
m − s<
2
σ
2
σ
2
dctjnd (um , vm )
2
dctjnd (um , vm )
log(τ )
log(τ )
,
(19)
where (um , vm ) means the location of the master watermark. Let DTV (um , vm ) denote the
decision threshold value of the master watermark at frequency (um , vm ) in block j, i.e.
j
DTV (um , vm ) =
j
⎧1
δ B (m, s ) = ⎨
⎩0
σ
2
2 dctjnd j (u m , vm )
if
if
log(τ )
. Then Eq. (19) can be rewritten as:
m − s≥ DTV j (um , vm )
m − s< DTV j (um , vm ) .
91
(20)
Improving Watermark Performance by Using Bipolar Split
However, there still exists one problem that is how to estimate the noise variance σ 2 . We
know that the embedded watermark value is decided by comparing the relative modification
values of the master and the slave watermarks. For the two watermarks, the common part of the
attack disturbances does not cause threat. So, we can estimate the noise variance by calculating
the overshoot or the undershoot amount from the noise-free case, i.e.
σ 2 = 2dctjnd j (um , vm ) − [(C ∗j (um , vm ) − C j (um , vm )) − (C ∗j (u s , vs ) − C j (u s , vs ))] .
(21)
The extraction steps are described as follows.
Step 1 : Both the host image and the image in question are DCT transformed to obtain
coefficients C and C*, respectively.
Step 2 : The same as the insertion step, we can find out the embedded master and slave
watermark positions (xm, ym) and (xs, ys), respectively.
Step 3 : According to equations (22) and (23) the master watermark wjm(i) and the slave
watermark wjs(i) can be extracted, individually.
⎧1
w jm (i) = ⎨
⎩0
w js (i )
if (C ∗j ( xm , ym ) − C j ( xm , ym )) − (C ∗j ( xs , ys ) − C j ( xs , ys )) ≥ DTV j ( xm , ym )
(22)
otherwise
(23)
= w jm (i )
Step 4 : The original watermark is obtained by further permuting the extracted watermark to
complete one whole cycle.
5. The experimental results
To verify the performance of the proposed watermarking scheme two experimental series
adopting the image-type and the random-sequence-type watermarks are conducted, respectively.
The first experimental series focuses on the image-type watermark. Various general attacks
including JPEG compression, blurring, sharpening, noise contamination, cropping, scaling and
rotation are tested to examine the survival rate of the embedded watermark. The NC value (Hou,
1998; Chen, 2000) defined in Eq. (24) is applied to measure the similarity between the referenced
watermark W and the extracted watermark W*.
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理工研究學報
M1 M 2
NC =
∑
∑ w(i, j ) × w (i, j )
i= j=
1
1
M1 M 2
∑ ∑ w (i, j )
i =1 j =1
M1 M 2
*
2
×
∑∑
i j
=1
=1
(1
− w(i,
M1 M 2
∑∑
i j
=1
(1
=1
j ))
× (1 − w * (i,
− w(i,
j ))
2
j ))
(24)
The JPEG compression attacks on the watermarked image are shown Table 1. Observing Table
1, we can find that if the compression quality is better than 20%, the embedded watermark is almost
not threatened. Even when the compression quality is down to 5% the extracted watermark is still
faintly visible. The blurring and sharpening attacks on the watermarked images are shown in Fig. 2
and Table 2. We note that our method is very robust to such types of attack. This owes to that the
neighboring insertion of the master and the slave watermarks can take advantage of the local
coherence of the DCT coefficient disturbance coming from blurring and smoothing. As to noise
attacks, we can find that even the noise variance grows to 25 and the corresponding image quality
PSNR is down to 20, the extracted watermark is still visible. These results are demonstrated in Table
3. Table 4 depict the results when the watermarked images are attacked by crop of different ratios.
Because the embedded watermark coefficients are almost uniformly distributed on the whole image,
the NC values of the extracted watermarks are almost proportional to the crop ratios. For scaling
attack, we test the reduction to one quarter of the original size. The detected NC value is 0.8055.
This means that the proposed scheme is insensitive to scaling attacks. For rotation attacks, the
detected watermarks and the corresponding NC values are listed in Table 5. These results show our
scheme is also robust to rotation attacks. In addition to the general attacks, some special
image-processing attacks are also examined. Table 6 shows various image-processing attacks such
as ripple, facet, film grain, print and scan, brightness/contrast adjustment and texturization. For
print-and-scan attack, the watermarked image was printed out first by
printer and then scanned by
scanner. As can be seen, all these detected
watermarks are recognizable. This reflects that the proposed scheme is robust to these
image-processing attacks, too. To understand if the proposed bipolar split scheme can achieve
comparable performance to embedding double-strength watermark without bipolar split, we
conduct another experimental series, which uses single watermark with double strength in the
watermark insertion step. Tables 7 to 12 depict the tested results. From these results, we can find
that the robustness of our method approaches to that of the double-strength scheme for compression
and noise attacks. And, their performance looks almost the same for rotation and cropping attacks.
Surprisingly, it can be noted that our method is even superior to the double-strength scheme for
EPSON Stylus Color 480
Microtek ScanMaker v310
93
Improving Watermark Performance by Using Bipolar Split
blurring, sharpening, scaling and print-and-scan attacks. This proves that our bipolar split scheme
can achieve comparable robustness to the double-strength scheme. However, remember that our
method can get 3dB better in PSNR than the double-strength scheme. The following test belongs to
image quality test. Twenty blocks were randomly picked out from the test image. These selected
blocks are embedded bipolar watermarks and double-strength watermarks in turn. Fig. 3 shows the
corresponding PSNR for the twenty blocks. As might be expected, our bipolar split scheme can gain
about 3dB quality superiority over the double-strength method.
The second experimental series focuses on the random-sequence-type watermark. We compare
the proposed watermarking schemes to the image-adaptive approach (Podilchuk & Zeng, 1998)
through examining watermark robustness to JPEG2000 compression, JPEG compression, blurring,
sharpening, noise contamination, cropping, rotation, rescaling and print-and-scan attacks. Here, the
normalized correlation coefficient between the embedded watermark and the detected watermark
(Cox et al., 1997) is employed to measure the similarity, denoted as SIM. Table 13 shows the
detected results after JPEG2000 compression. And, Tables 14 to 19 show the detected results after
various attacks. The detection results using our energy reallocation scheme are consistently better
than that using the image-adaptive approach for all these attacks. In other words, there are gains in
using energy reallocation watermarking scheme even for JPEG2000 compression – a tougher
challenge.
6. Conclusions
In this paper, we propose a new image watermarking scheme to promote the watermarking
robustness without sacrificing the imperceptibility. This profit results from splitting the watermark
strength into a bipolar manner. To take advantage from the locally correlated attacks, e.g. smoothing
and sharpening, the bipolar watermarks are embedded at neighboring DCT coefficients. The
experimental results show that the proposed method indeed gain about 3dB benefit in PSNR
compared with the single watermark with double strength method. For random-sequence-type
watermark, our method can also outperform the traditional appraches.
Acknowledgements
This work was partially supported by the National Science Council under grant number NSC 96-2221-E-024-016.
The author also would like to thank Mr. Ko-Chian Hsu for his assistance in designing the code.
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理工研究學報
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Submission Date:2010/02/11
Revision Date:2010/03/15
Acceptance Date:2010/04/09
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理工研究學報
DCT transformation Cj(u,v)
averaging
original image
8×8
block
partition
spatial JND
calculation
Pj(u,v)
attacking
simulation
DCT
Cj-(u,v)
frequency JND
calculation
Cj+(u,v)
DCTJND (u,v)
JNDj(x,y)
j
Fig. 1. The derivation process of the
domain
.
Table 1. The values of the extracted watermarks using the proposed method for
compression attacks.
Quality
80%
40%
20%
10%
5%
36.24
33.92
32.18
29.98
27.16
DCT
JND
NC
JPEG
PSNR
Extracted
Watermark
1.00
NC
(a)
0.95
0.81
(b)
0.60
0.41
(c)
(d)
(e)
(f)
Fig. 2 the images after blurring and sharpening, where (a) to (c) correspond to blurring attacks
of different levels and (d) to (f) correspond to sharpening attacks of different levels.
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Improving Watermark Performance by Using Bipolar Split
Table 2. The values of the extracted watermarks using the proposed method for blurring
and sharpening attacks.
Corresponding
(a)
(b)
(c)
(d)
(e)
(f)
to Fig. 2
37.65
33.91
31.15
32.60
22.33
18.01
NC
PSNR
Extracted
Watermark
1.00
NC
0.96
0.77
1.00
0.96
0.89
Table 3. The values of the extracted watermarks using the proposed method for noise
contamination attacks.
Variance
5
10
15
20
25
33.46
28.05
24.67
22.20
20.32
NC
PSNR
Extracted
Watermark
0.95
NC
Table 4. The
Ratio
NC
0.83
0.70
0.62
0.57
values of the extracted watermarks using the proposed method for cropping.
80%
60%
50%
40%
20%
Extracted
Watermark
0.83
NC
0.61
0.51
0.41
0.20
Table 5. The values of the extracted watermarks using the proposed method for rotation
attacks.
Degrees
20
30
40
50
60
0.87
0.84
0.83
0.83
0.84
Extracted
Watermark
NC
NC
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理工研究學報
Table 6. The values of the extracted watermarks using the proposed method for various
special image-processing attacks
NC
Ripple
Facet
=30.1
Film Grain
=30.8
PSNR
=16.2
PSNR
=0.81
=0.85
NC
Print and Scan
=17.3662
=0.56
NC
Brightness/Contrast
adjustment
=12.2832
PSNR
PSNR
NC
Texturization
=19.183
PSNR
PSNR
=0.52
=0.71
NC
=0.64
NC
NC
Table 7. The values of the extracted watermarks using single watermark with double
strength method for JPEG compression attacks.
Quality
80%
40%
20%
10%
5%
34.74
33.01
31.59
29.63
26.95
NC
PSNR
Extracted
Watermark
NC
1.00
0.99
0.90
99
0.73
0.48
Improving Watermark Performance by Using Bipolar Split
Table 8. The values of the extracted watermarks using single watermark with double
strength method for blurring and sharpening attacks.
Attack
Blur_1
Blur_2
Blur_3 Sharpen_1 Sharpen_2 Sharpen_3
36.06
33.32
30.95
31.53
22.03
17.75
NC
PSNR
Extracted
Watermark
0.99
NC
0.93
0.71
0.99
0.93
0.82
Table 9. The values of the extracted watermarks using single watermark with double
strength method for noise contamination attacks.
Variance
5
10
15
20
25
32.62
27.83
24.55
22.15
20.28
NC
PSNR
Extracted
Watermark
0.99
NC
0.91
0.81
0.73
0.68
Table 10. The values of the extracted watermarks using single watermark with double
strength method for cropping.
Ratio
80%
60%
50%
40%
20%
NC
Extracted
Watermark
0.83
NC
0.61
0.51
0.41
0.20
Table 11. The values of the extracted watermarks using single watermark with double
strength method for rotation attacks.
Degrees
20
30
40
50
60
NC
Extracted
Watermark
NC
0.87
0.84
0.83
100
0.83
0.85
理工研究學報
Table 12. The values of the extracted watermarks using single watermark with double
strength method for scaling and print-and-scan attacks.
Attack
Quarter-size Print-and-scan
NC
Extracted
Watermark
NC
0.78
0.48
Fig. 3 Watermarked image quality comparison for the proposed method and the
double-strength scheme, where ‘×’: the former, ‘•’: the latter, the X-axis: different blocks
and the Y-axis: PSNR.
Table 13. The comparison of the extracted watermark similarity for JPEG2000 compression
attacks, where (1): Podilchuk’s scheme, (2): the proposed method.
Compression
80%
40%
20%
10%
Quality
SIM of (1)
0.99
0.94
0.66
0.26
SIM of (2)
0.99
0.97
0.78
0.35
Table 14. The comparison of the extracted watermark similarity for JPEG compression attacks,
where (1): Podilchuk’s scheme, (2): the proposed method.
Compression
80%
40%
20%
10%
5%
Quality
SIM of (1)
1.00
1.00
0.98
0.94
0.79
SIM of (2)
1.00
1.00
0.99
0.97
0.91
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Improving Watermark Performance by Using Bipolar Split
Table 15. The comparison of the extracted watermark similarity for blurring and sharpening
attacks, where (1): Podilchuk’s scheme, (2): the proposed method.
Blur_1
Blur_2
Blur_3 Sharpen_1 Sharpen_2 Sharpen_3
SIM of (1)
0.98
0.86
0.54
0.96
0.83
0.68
SIM of (2)
0.99
0.94
0.73
0.98
0.93
0.83
Table 16. The comparison of the extracted watermark similarity for noise attacks,
where (1): Podilchuk’s scheme, (2): the proposed method.
Noise
5
10
15
20
25
Variance
SIM of (1)
0.99
0.98
0.96
0.93
0.90
SIM of (2)
1.00
0.99
0.98
0.97
0.95
Table 17. The comparison of the extracted watermark similarity for cropping attacks,
where (1): Podilchuk’s scheme, (2): the proposed method.
Cropping
80%
60%
50%
40%
20%
Ratio
SIM of (1)
0.95
0.88
0.82
0.77
0.56
SIM of (2)
0.95
0.86
0.79
0.75
0.55
Table 18. The comparison of the extracted watermark similarity for rotation attacks,
where (1): Podilchuk’s scheme, (2): the proposed method.
Rotation
20
30
40
50
60
Degrees
SIM of (1)
0.60
0.51
0.49
0.53
0.55
SIM of (2)
0.69
0.59
0.63
0.66
0.68
Table 19. The comparison of the extracted watermark similarity for scaling and print-and-scan
attacks, where (1): Podilchuk’s scheme, (2): the proposed method.
Attacks Quarter size Print and scan
SIM of (1)
0.66
0.68
SIM of (2)
0.83
0.78
102