Proc. of the International Conference on Computer & Communication Engineering 2014 (ICCCE 2014)
23-25 September 2014, Kuala Lumpur, Malaysia
Robust Image Watermarking Scheme by Discrete
Wavelet Transform
Mohammad Abdullatif1
Othman O. Khalifa
2 Akram M. Zeki
3
R.F. Olanrewaju4
1,2,4Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia
3Department of Information System, International Islamic University Malaysia, Kuala Lumpur, Malaysia
Abstract— This paper proposes a robust digital image
watermarking techniques by using discrete wavelet transform.
The watermark is embedded in 2-level diagonal coefficient to
achieve high performance in term of imperceptibility. The
watermark is embedded by selecting a seed that generating
pseudorandom noise sequence. Then the Watermark is
extracted back by using the same pseudorandom noise
sequence seed in the embedding. The experimental results
show that the proposed algorithm provides good level of
imperceptibility. Moreover, the watermarked image is robust
against several attacks.
Keywords: Digital Image Watermarking, Discrete Wavelet
Transform, Pseudorandom Noise Sequence, Watermarking
Attacks.
I. Introduction
Since the Internet has become very popular, and people
can share whatever they want to share such as images,
videos, documents, etc., there has been a need to protect
publishing copyright. In addition, there has been also a
significant demand for information security. For these
reasons and other reasons, digital image watermarking has
become very popular recent years as a good solution for
these cases. Many researches have gone through this field
to create new techniques, and to enhance current
techniques as proper solutions for previous problems.
Digital image watermarking techniques stand on
embedding a host image with information which is called
watermark, then the watermarked image will be
transmitted, and can be extracted at the receiver. There are
two kinds of detection types at the receiver. The first type
is called blind watermarking, because the detector doesn’t
need the original cover image to detect the watermark. The
second type is called non-blind and it needs the original
cover image to extract the watermark [1].
The basic model of digital image watermarking
consists of two parts; the first part is the watermark
embedding process which shown in Fig. 1, and the second
part is the watermark detection process which shown in
Fig. 2.
Fig. 1. Watermark Embedding
Fig. 2. Watermark Detection
In Fig. 1, which represents sender, the Watermark is
embedded into the Cover Image with the Secret Key that
ensures the security of watermarking process. The output
is the Watermarked Image. In Fig. 2, at the receiver side,
the detector detects the watermark from the Watermarked
Image by using the Secret Key to recover the Watermark
[2].
Digital image watermarking concerns to solve some
issues properly, thus, this paper highlights the main
requirements of watermarked image as following:
Cover Image Watermarked
Image
Watermark
Embedding
Watermark
Secret
Key
Watermarked
Image Watermark
Watermark
Detection
Secret
Key
Proc. of the International Conference on Computer & Communication Engineering 2014 (ICCCE 2014)
23-25 September 2014, Kuala Lumpur, Malaysia
A- Robustness:
The robustness is the ability of detecting the watermark
after some signal processing modification such as spatial
filtering, scanning and printing, lossy compression,
translation, scaling, and rotation [1], and other operations
like digital to analog (D/A), analog to digital (A/D)
conversions, cutting, image enhancement [3]. In addition,
not all watermarking algorithms have the same level of
robustness, Some techniques are robust against some
manipulation operations, however, they fail against other
stronger attacks [4]. Moreover, it’s not always desirable
for watermark to be robust, in some cases; it’s desired for
the watermark to be fragile [1]. Therefore, the robustness
can be classified as following:
Robust: The watermark is designed to be able to
survive against intentional and incidental attacks
[5]. This kind of watermarking can be used in
broadcast monitoring, copyright protection,
fingerprinting, and copy control [6].
Fragile: The watermark in this type is designed to
be destroyed at any kind of modification, to detect
any illegal manipulation, even slight changes,
involving incidental and intentional attacks.
Fragile watermarks are mainly used in content
authentication and integrity verification. They use
blind detection type [6], as it will be discussed in
Detection Types. In addition, the implementation
of fragile techniques is easier than the
implementation of robust ones [7].
Semi-fragile: The watermark in this type is robust
against incidental modifications, but fragile
against malicious attacks [8]. And it is used for
image authentication [9].
B- Imperceptibility:
Imperceptibility (also known as Invisibility and
Fidelity) is the most significant requirement in
watermarking system, and it refers to the perceptual
similarity between the original image before watermarking
process and the watermarked image [1]. In other words,
the watermarked image should look similar to the original
image, and the watermark must be invisible in spite of
occurrence of small degradation in image contrast or
brightness. However, the challenge is that imperceptibility
could be achieved, but the robustness and the capacity will
be reduced, and vice versa, imperceptibility may be
sacrificed by increasing the robustness and the capacity
[4]. Moreover, the watermark not always desired to be
invisible, sometimes, it is preferred to have visible
watermark into the image [6].
C- Capacity:
Capacity (also known as Payload) refers to the number
of bits embedded into the image. The capacity of an image
could be different according to the application that
watermark is designed for [1]. Moreover, studying the
capacity of the image can show us the limit of watermark
information that would be embedded and at the same time
satisfying the imperceptibility and robustness [4].
II. Related Work C. Patvardhan and others [10] proposed a new method
of digital image watermarking using Discrete Wavelet
Packet Transform (DWPT) analysis of the host image. The
binary watermark is embedded in the high frequency
diagonal components of wavelet packet decomposition
tree which have maximum entropy because human eyes
are less sensitive in high frequency bands having
orientation of 450. Watermarking is achieved by
generating a pseudo-random sequence and then embedding
it into wavelet coefficients according to the watermark bit
pattern. The results showed that this algorithm provides a
good level of imperceptibility and robustness against
various types of attacks. However, the watermark
embedded in this method is of size 21x21, and this
watermark is considered very small to be embedded in the
image.
P. Kumhom [11] proposed a new technique based on a
selection of a high frequency range containing large
amount of information. The selected high-frequency range
contributes to the imperceptibility of the watermark while
the robustness against compression is achieved because the
selected frequency range contains large amount of
information. The entropy-based algorithm is adopted to
find the best tree of the wavelet packet transform (WPT).
Such best tree represents the best basis of the WPT whose
corresponding frequency sub-band contains high
information energy.
S. Zhenghua and others [12] proposed a technique
based on a selection of a high frequency range containing
large amount of information. The entropy-based algorithm
is used to find the best tree of the wavelet-based contourlet
packet transform (WCPT). Such best tree represents the
best basis of the WCPT whose corresponding frequency
sub-band contains high information energy. Moreover, the
security of the watermark is added by the pseudorandom
permutation of the bits of the watermark image before
embedding.
III. Discrete Wavelet Transform (DWT)
Wavelet Transform has been used widely since it has
been adopted in the established image coding standard
JPEG 2000 [2], and it produce considerably better quality
for decoded image than JPEG. The main advantage that
DWT has over Fourier transforms is temporal resolution. It
captures both location and frequency information .The
basic idea of DWT is to separate frequency detail, which is
multi-resolution decomposition. One time of
decomposition can divide the image to four sub images at
Proc. of the International Conference on Computer & Communication Engineering 2014 (ICCCE 2014)
23-25 September 2014, Kuala Lumpur, Malaysia
a quarter sizes. They are a low frequency approximate sub
image, and three horizontal, vertical, and diagonal
direction high frequency details sub images [13].
Fig. 3. two-level DWT
Fig. 3 is an example diagram of two-level DWT
decomposition. In the wavelet transform domain, high
frequency parts represent detailed information of image's
edge, contour and texture and so on. It’s not easy to detect
the watermark in these places as people are not easily able
to recognize it. But after processing or attacking, it doesn’t
have good stability. Most energy of image is centralized in
low frequency. Low frequency coefficients are nearly not
changed by common attacks so that watermarking
information embedded in low frequency coefficients has
better robustness [13].
IV. Watermark Embedding and Extraction Method
A- Watermark Embedding Algorithm
1- Input: A Grayscale cover image (I), and a binary
watermark (W).
Output: Watermarked Image.
2- Decompose the cover image in 2-level discrete
wavelet transform (DWT) of Host Image (I).
3- D2 is considered for watermark embedding.
4- Convert the 2D watermark into 1D array.
5- Select a Seed of type RNG to generate a PN sequence
PNS of size equal to the size of selected matrix D2
6- Generate another matrix PN from PNS according to
the relation PN = R1 × (PNS − R2), where, R1 = 2
and R2 = 0.5 to change the PNS from 0s and 1s to 1s
and -1s.
7- If watermark bit is 0 (Black) then modify elements of
the selected matrix as, D2 = D2 + k.PN, Where, k is
embedding strength. If watermark bit is 1 (white) then
wavelet coefficients will not be changed.
8- Repeat the step 6 and 7 for all ‘0’ watermark bits with
every time newly generated PN sequence.
9- Put the modified D2 matrix in its original position in
wavelet tree and take inverse discrete wavelet
transform IDWT to get back the watermarked image
(Iw).
The embedding algorithm is shown graphically in Fig. 4.
Fig. 4. Watermark Embedding Algorithm
B- Watermark Extraction
1- Input: Watermarked Image (𝐼𝑤).
2- Output: Extracted Watermark (𝑊𝑅).
3- Decompose the watermarked image (𝐼𝑤) in 2-level
DWPT and get back the same modified coefficient
metrix D2′.
4- Generate the same PN sequence (𝑃𝑁𝑆), which was
generated in embedding process using the same Seed
of type RNG with same value.
A2 H2
V2 D2
V1 D1
H1
Cover Image
(I)
2-Level
DWT
Seed
PNS
Watermark Embedding
cD2,3 = cD2,3 + k.PN
Watermark
(W)
Convert W
to vector
PN = R1*(PNS-R2)
2-level
IDWT
Watermarked
Image
Proc. of the International Conference on Computer & Communication Engineering 2014 (ICCCE 2014)
23-25 September 2014, Kuala Lumpur, Malaysia
5- Convert the PN sequence (𝑃𝑁𝑆) into 𝑃𝑁 = 𝑅1× (𝑃𝑁𝑆
− 𝑅2) with 𝑅1 = 2 𝑎𝑛𝑑 𝑅2 = 0.5.
6- Compute the correlation coefficients between PN
sequence (𝑃𝑁) and modified coefficient matrix D2′, as
𝑟 = 𝑐𝑜𝑟𝑟2(𝑃𝑁, 𝐷2′) .
7- Repeat step 6 for all watermark bits and compute the
correlation values.
8- Compute the threshold value as (𝑇=1.4 * 𝑚𝑒𝑎𝑛 (𝑟))
and initialize a row vector (𝑊′) having all values ‘1’
equivalent to the size of the watermark.
9- For every watermark bit, compare 𝑟 with 𝑇, then
𝑊′=0 if r > T, and 𝑊′=1 if r < T.
10- Reshape the row matrix 𝑊′ into a matrix equivalent to
the size of original watermark matrix to get recovered
watermark.
Fig. 5. Watermark Extraction Algorithm
V. EXPERIMENTAL RESULTS This section discusses the results of the proposed
algorithm. Five images of size 512 x 512 were used as
cover images, which are Bridge, Peppers, Cameraman,
Boat, and Houses. They are shown in Fig. 6.
a b
c d
e
Fig. 6. Cover images
a: Bridge, b: Peppers, c: Cameraman, d: Boat, e: Houses
Two binary images of size 16 x16 and 32 x 32 were
used as watermarks. They are shown in Fig. 7.
a b
Fig. 7. Watermark images: a: size 16 x16, b: size 32 x32
If r > T
Watermarked
Image Seed
2-Level DWT PNS
PN = R1*(PNS-R2)
Compute Correlation
𝑟 = 𝑐𝑜𝑟𝑟2 (𝑃𝑁, 𝐷2′)
T = 1.4 * mean(r)
Extracted Watermark
W’ = 0 W’ = 1
Yes No
Proc. of the International Conference on Computer & Communication Engineering 2014 (ICCCE 2014)
23-25 September 2014, Kuala Lumpur, Malaysia
The performance of the proposed method is evaluated
in term of imperceptibility and robustness. The
imperceptibility is measured by determining the difference
between the original image and the watermarked image.
Peak Signal to Noise Ratio (PSNR) is commonly used
measure for imperceptibility level. The PSNR is defined
as:
𝑃𝑆𝑁𝑅 (
𝑆 )
For two images having 8 bits per pixel, The PSNR is
defined as:
𝑃𝑆𝑁𝑅 𝑜 (
𝑆 )
Where MSE is Mean Squared Error and it’s defined as:
𝑆
𝑁∑ 𝐼 [ ] 𝐼 [ ]
Where:
N: number of pixels in each image
: Original image
: Watermarked image
The PSNR which is usually expressed in decibel scale
(dB) represents the ratio between the maximum pixel
value of an image and the corrupting noise (defined via
MSE) that affect its representation. The Typical range for
PSNR values is between 30 and 50 dB, considering that
higher value is better where the differences are between
the two images are less and indistinguishable by human
eyes [14].
Table 1: PSNR values for k = 1, 1.5, 2
Image Watermark
size
PSNR (dB)
k = 1 k = 1.5 k = 2
Bridge 16x16 44.066 40.594 38.154
32x32 38.257 34.874 32.512
Peppers 16x16 43.069 39.583 37.133
32x32 37.104 33.699 31.352
Cameraman 16x16 44.091 40.636 38.204
32x32 38.207 34.800 32.415
Boat 16x16 43.973 40.459 37.969
32x32 38.165 34.853 32.560
Houses 16x16 44.141 40.711 38.302
32x32 38.289 34.922 32.575
The robustness is measured by performing some
attacks such as Disk filter, Average filter, and Gaussian
noise. Then Normalized Correlation (NC) between the
embedded and extracted watermarks is calculated for
embedding strength (k = 1, 1.5, 2).
The NC is defined as following:
𝑁 ∑ ∑ 𝑊 𝑊
√∑ ∑ 𝑊 √∑ ∑ 𝑊
Table 2: Normalized Correlation (NC) values under Disk
Filter
Disk Filter Watermark
size
NC
k = 1 k = 1.5 k = 2
Bridge 16x16 0.831 0.948 1.000
32x32 0.835 0.948 0.975
Peppers 16x16 1.000 1.000 1.000
32x32 0.964 0.993 1.000
Cameraman 16x16 0.939 0.982 1.000
32x32 0.936 0.980 0.991
Boat 16x16 0.923 0.991 1.000
32x32 0.911 0.965 0.978
Houses 16x16 0.789 0.925 0.991
32x32 0.768 0.905 0.971
Table 3: Normalized Correlation (NC) values under
Average Filter
Average
Filter
Watermark
size
NC
k = 1 k = 1.5 k = 2
Bridge 16x16 0.713 0.850 0.941
32x32 0.689 0.858 0.927
Peppers 16x16 0.965 0.991 1.000
32x32 0.926 0.975 0.991
Cameraman 16x16 0.865 0.947 1.000
32x32 0.872 0.956 0.982
Boat 16x16 0.875 0.982 1.000
32x32 0.844 0.938 0.975
Houses 16x16 0.753 0.871 0.949
32x32 0.624 0.802 0.888
Table 4: Normalized Correlation (NC) values under
Gaussian Noise
Gaussian Watermark
size
NC
k = 1 k = 1.5 k = 2
Bridge 16x16 0.882 0.982 1.000
32x32 0.910 0.969 0.995
Peppers 16x16 1.000 1.000 1.000
32x32 0.991 1.000 1.000
Cameraman 16x16 0.982 1.000 1.000
32x32 0.971 0.991 1.000
Boat 16x16 0.982 1.000 1.000
32x32 0.956 0.978 0.991
Houses 16x16 0.885 0.991 1.000
32x32 0.858 0.954 0.980
Proc. of the International Conference on Computer & Communication Engineering 2014 (ICCCE 2014)
23-25 September 2014, Kuala Lumpur, Malaysia
Conclusion In this paper, a robust digital image watermarking
scheme by using discrete wavelet transform is proposed.
The watermark is embedded in 2-level diagonal coefficient
to achieve high performance in term of imperceptibility.
Robustness is also evaluated by performing some attacks
such as Disk filter, Average filter, and Gaussian noise. The
experimental results show that the proposed algorithm
provides good level in term of imperceptibility and
robustness. Two different sizes of watermarks were used
16 x 16 and 32 x 32 to show the capacity level of the
proposed algorithm.
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