1. technology and computing
2. consumer electronics
3. tv and video equipment
4. televisions
5. hdtvs

An Improved Blind Watermarking Method in
Frequency Domain for Image Authentication
Md. Iqbal H. Sarker
M. I. Khan
Department of Computer Science & Engineering
Chittagong University of Engineering & Technology
Chittagong-4349, Bangladesh
E-mail: iqbal@cuet.ac.bd
Department of Computer Science & Engineering
Chittagong University of Engineering & Technology
Chittagong-4349, Bangladesh
E-mail: Muhammad_ikhan@cuet.ac.bd
Abstract—Digital watermarking has been widely used for
copyright protection for multimedia data. This paper proposes a
new watermarking method of image for copyright protection
based on Hadamard transform. This method can embed or hide
an entire image or pattern as a watermark such as a company’s
logo or trademark directly into original image for copyright
protection. This watermarking method deals with the extraction
of the watermark information in the absence of original image,
hence the blind scheme is obtained. The experimental results
prove that our proposed watermarking method offers better
image quality and more robustness under various attacks such as
JPEG compression, cropping, sharpening, filtering and so on.
Peak Signal to Noise Ratio (PSNR) and Normalized Correlation
Coefficient (NCC) are computed to measure image quality and
robustness. Finally, a comparative study is made against the
previous technique.
Keywords—Digital image watermarking; Best first search
algorithm; Hadamard transform; Arnold Transform.
I. INTRODUCTION
Digital watermarking is a technique that embeds additional
information called watermark into a multimedia object in
order to secure it. A watermark is a hidden signal added to
images that can be detected or extracted later to make some
affirmation about the host image. When digital media is
flourishing with notable advancement in recent years, the
distribution of unauthorized copies of media content is also
increasing day by day. Because of easy access to digital
content the online purchasing and distribution become easier.
Thus it is an urgent need to provide protection for digital
content. One solution that gaining popularity in protecting the
digital content is digital watermarking.
A robust watermark withstands malicious attacks; such as
scaling, rotation, filtering and compression. This kind of
watermarking is used for copyright protection. Fragile
watermark can detect any unauthorized modification in an
image and therefore they are quite suitable for an
authentication purpose. Watermarking can be classified in
several ways. According to the need of original image for
watermark extraction or detection, watermarking is classified
to non-blind and blind watermarking techniques. In non-blind
technique, it requires the original image when detecting the
watermark whereas in blind technique no need the original
image. There are different approaches of digital image
watermarking like as spatial-domain and frequency domain
technique. In frequency domain, the watermark is embedded
by changing the frequency coefficients. The most used
transforms are DWT, DCT, DFT and Hadamard
Transformation (HT). In spatial domain, the watermark is
embedded directly by modifying the intensity values of pixels.
Spatial domain watermarking technique is easier and its
computing speed is high than transform domain watermarking.
But the disadvantage is that it is not robust against common
image processing operations.
In this paper, we propose a robust watermarking method
based on Hadamard Transform. The simplicity of Hadamard
transform offers a significant advantage in shorter processing
time and ease of hardware implementation than most
orthogonal transform techniques such as DWT and DCT.
II. RELATED WORK
Anthony T.S.Ho proposed a watermarking system for
digital image in frequency domain using Fast Hadamard
Transform [1]. Saeid Saryazdi and Hossein Nezamabadi-pour
developed a new blind gray-level watermarking scheme.
Hence the host image is first divided into 4×4 non-overlapping
blocks. For each block, two first AC coefficients of its
Hadamard transform are then estimated using DC coefficients
of its neighbor blocks [2]. K. Deb, M.S. Al-Seraj, M.M.
Hoque, M.I.H. Sarker proposed a combined DWT and DCT
based watermarking technique with low frequency
watermarking with weighted correction [3]. F. Hosain
presented a survey of various digital watermarking techniques
for multimedia data such as text, audio, image and video for
copyright protection [4]. Ali Al-Haj described an
imperceptible and a robust combined DWT-DCT digital image
watermarking algorithm for copyright protection [5]. Zhao
Rui-mei, Lian Hua, Pang Hua-wei, Hu Bo-ning proposed a
blind watermarking algorithm based on DCT for digital image
in which a 2-bit image embeds in a 8-bit gray image [6]. R.
Kountchev, S. Rubin, M. Milanova,V. Todorov proposed a
978-1-4799-0400-6/13/$31.00 ©2013 IEEE
new method where the still digital image is transformed using
Complex Hadamard Transform (CHT) [7]. S.J. Lee, S. H.
Jung proposed a survey of watermarking systems which are
applied to Multimedia [8]. Dr. M. Sathik and S. S. Sujatha
described the watermark construction process where
scrambled version of watermark is obtained with the help of
Arnold transform [9].
III. HADAMARD TRANSFORM
The Hadamard transform (also known as the Walsh–
Hadamard transform, Walsh transform or Walsh–Fourier
transform) is an example of a generalized class of Fourier
transforms. The Hadamard matrix H m represents a M × M
m
Hadamard matrix, where M = 2 , m = 1, 2, 3… with
element values either +1 or -1, the rows and columns of H m
are orthogonal. Now a Hadamard matrix is given by [1]:
⎡ H m−1
Hm = ⎢
⎣⎢ H m−1
H m−1
− H m−1
⎤
⎥
⎦⎥
Where, m > 0 . Now for m=1, M=2. Hence
matrix is given by:
⎡1
H =⎢
⎣⎢1
(1)
2 × 2 Hadamard
1 ⎤
⎥
− 1⎦⎥
A digital image can be considered as a two unit function f(x,y)
in the plane Z. It can be represented as Z = f(x,y) where x,y
∈ {0,1,2,3....N-1} . Hence, N represents order of digital
image. The matrix of image can be changed into a new matrix
by using the Arnold transform which results in a scrambled
version to offer better security. It is a mapping function which
changes a point (x,y) to another point ( x ′ , y ′ ) by using the
equation.
x ′ = (x + y) mod N
y ′ = (x + 2y) mod N
V. PROPOSED WATERMARKING METHOD
In this section, we describe our proposed method of
watermarking. This watermarking method consists of two
process; watermark embedding process and watermark
extraction process.
A. Embedding Process
The proposed watermark embedding process is shown in
Figure 1. To embed the watermark into the host image
following steps are required.
Watermark image
Original image
(2)
Let [O ] represents the original image and [T ] the transformed
image, the 2D-Hadamard transform is given by [1]:
H [O ]H m
[T ] = m
M
IV. ARNOLD TRANSFORM
Apply Arnold transform
Divide the image into 8x8
sub-blocks
Divide the image into 8x8
sub-blocks
Apply Hadamard transform
Apply Hadamard transform
(3)
Apply Best-first-search
algorithm
The inverse 2-D Hadamard transformation (IHT) is given as:
[O] = H −1 m [T ]H * m =
H m [T ]H m
M
(4)
In our algorithm the processing is performed on 8 × 8
sub-blocks of whole image, so we use the third order
Hadamard matrix (H3) and H 3 becomes:
⎡1
⎢1 −
⎢
⎢1
⎢
1−
H = ⎢
⎢1
⎢
⎢1 −
⎢1
⎢
⎢⎣1 −
1 1 1⎤
⎥
1 1−1 1−1 1 −1 ⎥
1−1−1 1 1−1 −1⎥
⎥
1−1 1 1−1−1 1 ⎥
1 1 1−1−1−1 −1⎥
⎥
1 1−1−1 1−1 1⎥
1−1−1−1−1 1 1⎥
⎥
1 − 1 1 − 1 1 1 − 1 ⎥⎦
1
1
1
Co-efficient
within range:
0<x<1 &
-1<x< 0
Select highest value of
longest increasing sequence
Watermark embed equation
f ( x, y) = α ∗ w(i, j )
Visited
matrix
Key matrix
1
Apply inverse Hadamard
transform
(5)
Watermarked image
Fig.1 Flow Diagram for Watermark Embedding Process
1) Divide the Host Image: The host image is divided into
8 × 8 non-overlapping blocks.
2) Apply Hadamard Transform: Hadamrd transformation
is performed on each sub-blocks of the host image. For each
sub-block, 64 Hadamard transform co-efficients are obtained.
Each block has a DC value at upper-left corner which is
avoided for embedding. So we have 63 coefficients left for
embedding.
3) Apply Best-first-Search Algorithm: Now the best-firstsearch algorithm is applied to find the increasing sequence of
each block to embed watermark. In figure 2, C is the main
point from which we start searching and 8, 5, 6 are the order
of searching the coefficients and we get some increasing
sequences. From these sequences efficient point is selected by
using a priority queue.
1
2
3
4
5
6
7
8
CC
applied to all matrixes using the equation (4) to produce the
watermarked image.
B. Extraction Process
The proposed watermark extraction process is shown in
figure 3. To extract the watermark image from watermarked
image following steps are required.
1) Apply Hadamard Transform: The watermarked image
is divided into 8*8 sub-blocks and hadamard transformation is
applied in each block.
Watermarked image
Divide the watermarked image into 8x8
sub-blocks
Apply Hadamard transform on each subblock
Extract the watermark point using visited
matrix
Fig. 2 Order of Searching
4) Selection of Longest Increasing Sequence: From these
increasing sequences, longest sequence is selected. The
highest value of this longest increasing sequence is used for
embedding in our proposed method.
5) Perform Arnold Transform: Arnold transform is
performed to scramble the elements of watermark image and
obtain new matrix M. Then M is divided into 8 × 8 nonoverlapping sub-blocks.
6) Apply Hadamard transform: Hadamard transform is
also applied on each sub-block of watermark image. From
Hadamard transform coefficients of watermark image, points
are selected within the range: 0<x<1 or -1 <x<0 and stored in
a visited matrix. The rest of the components stored in a key
matrix which is used in extraction process.
Watermark extraction equation:
w′(i, j ) =
f ′( x, y)
α
Store the co-efficient on new matrix
Divide the matrix into 8x8 blocks
Apply inverse Hadamard transform on
each block
Perform Anti-Arnold Transform
7) Apply Embedding Equtation: Then we have to use the
following equation for embedding process.
Extract watermark image
f ( x, y) = α ∗ w(i, j )
(6)
Where w(i, j ) denotes watermark Hadamard co-efficient,
f ( x, y ) denotes watermarked Hadamard co-efficient and α
denotes the scaling factor, which defines the strength of the
watermark.
8) Apply Inverse Hadamard Transform: After performing
insertion in all sub-blocks the inverse Hadamard transform is
Fig.3 Flow Diagram of Watermark Extraction Process
2) Apply Extraction Equtation: The watermark image is
extracted using the visited matrix. Here we use the inverse
equation of (6) for extraction of watermark image.
w′(i, j ) =
f ′( x, y)
α
(7)
Where,
f ′( x, y ) denotes the watermarked co-efficient and
w′(i, j ) denotes the extracted watermark co-efficient. The
watermark Hadamard coefficients are extracted from all the
sub-blocks of watermarked image and stored it in a new
matrix along with key matrix.
3) Apply Inverse Hadamard Transform: Divide the new
matrix into 8*8 block and inverse Hadamard transform is
applied. Thus we obtain the extracted scrambling watermark
image
4) Perform Anti-Arnold Transform: Anti Arnold transform
is used to get the original watermark image from scrambling
image.
VI. EXPERIMENTAL RESULTS AND ANALYSIS
The peak signal to noise ratio (PSNR) is used to evaluate the
quality of the watermarked image in comparison with host
image. The PSNR formula is as follows:
PSNR = 10 log10
Where MSE =
255 × 255
MSE
TABLE I. PSNR AND NCC FOR NO ATTACK
Image
Name
Lena
For No Attack
PSNR
NCC
38.78
1
TABLE II. PERFORMANCE COMPARISON
Attack
NCC
Anthony’s
Method [1]
Proposed Method
Sharpening 3x3
0.9573
0.9933
1 row 1 column
removed
Frequency mode
laplacian removal
0.9866
0.9883
0.9580
0.9754
Scaling 7.5
0.9354
0.9554
JPEG compression
Of factor 30
Changing aspect
ratio
0.8688
0.9042
0.8199
0.8558
(8)
[I (i, j) − K (i, j)]
1 M −1
∑
M × N N −1
2
MSE defines mean square error. The M and N are the height
and width of the image respectively. I (i, j ) and K (i, j )
are the values located at coordinates (i ,j) of the host image
and the watermarked image.
(a)
(b)
(c)
(d)
After extracting the watermark, the normalized correlation
coefficient (NCC) is computed to measure the correctness of
an extracted watermark. It is defined as:
NCC =
1 m n
∑∑ w(i, j) × w′(i, j)
m × n i =1 j =1
(9)
Where, m and n are the height and width of the watermark
respectively. w(i, j ) and w′(i, j ) are the watermark bits
located at coordinates (i, j) of the original watermark and the
extracted watermark.
Among various test images employed in experiments, the 512
x 512 “Lena” image which is shown in Fig. 4 (a) is used to
show the effectiveness of the proposed method. The 64×64
watermark is shown in Fig 4 (b).
(e)
Fig.5 Different attacks (a) Salt & pepper (b) Cropping (c) Gaussian noise
(d) JPEG compression (e) Rotation
(a)
(b)
(c)
Fig.4 (a) Host image (b) Watermark image (c) Watermarked image
TABLE VII. PSNR AND NCC FOR CROPPING ATTACK
TABLE III. PSNR AND NCC AFTER APPLYING JPEG COMPRESSION
For cropping
For JPEG compression
Attack
Attack
PSNR
JPEG compression
(QF=30)
JPEG compression
(QF=60)
JPEG compression
(QF=90)
33.7029
0.9842
36.3765
0 .9909
38.4525
1
For filtering
Attack
PSNR
Weiner filtering
NCC
Cropping[32x32]
30.1313
0 .9982
Cropping[64x64]
25.4391
0 .9894
Cropping[128x128]
16.3047
0 .9595
VII. CONCLUSION
TABLE IV. PSNR AND NCC AFTER APPLYING FILTERING ATTACKS
Median filtering 3 × 3 )
PSNR
NCC
NCC
34.4961
0 .9873
36.2677
0.9711
TABLE V. PSNR AND NCC AFTER DIFFERENT ROTATION ATTACKS
In this paper, we proposed a new digital image
watermarking scheme based on hadamard transform
which provides a complete procedure that embeds and
extracts the watermark information effectively. Our
watermark embedding process does not degrade the
visual quality of the image. Moreover this authentication
process provides some qualities like imperceptibility,
robustness and security. The performance of the
watermarking scheme is evaluated with common image
processing attacks. The experimental results show that
our proposed method is efficient and more robust
against those malicious attacks.
For rotation
ATTACK
PSNR
NCC
VIII. REFERENCES
[1]
Rotation angle=5
24.1601
0.8875
Rotation angle=10
22.5655
0.9915
[2]
Rotation angle=20
21.4640
0.9956
[3]
TABLE VI. PSNR AND NCC AFTER APPLYING DIFFERENT NOISE
[4]
For noise
Attack
PSNR
NCC
Gaussian Noise
(Average=0,density=.002)
26.8266
0.9873
Speckle Noise
(density=.01)
26.5200
0.9887
Salt & pepper noise
(Strength=.01)
24.7564
0.9856
[5]
[6]
[7]
[8]
[9]
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