Robust Video Watermarking Algorithm Using Spatial Domain Against Geometric Attacks

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, 2010

Robust Video Watermarking Algorithm Using Spatial
Domain Against Geometric Attacks
Sadik Ali. M. Al-Taweel1, Putra. Sumari2, Saleh Ali K. Alomari1,2
1, 2
School of Computer Science, Universiti Sains Malaysia
11800 Penang, Malaysia
sadiq_altaweel@yahoo.com, putras@cs.usm.my, salehalomari2005@yahoo.com

II. RELATED WORK
Abstract— it is important for Digital watermarking to have
digital data and multimedia, such as video, music, text, and Some of the video watermarking techniques targeting
image copyright protection because of network and multimedia geometric attacks are on raw videos [4], [5]. Hartung and
techniques that easily copy. One of the significant problems in Girod proposed algorithm for uncompressed and compressed
video watermarking is the Geometric attacks. In this paper
new robust watermarking algorithm has been proposed, based video watermarking, based on the idea of spreading the
on spatial domain which is robust against geometric attacks watermark energy over all of the pixels in each of the frames.
such as downscaling, cropping, rotation, and frame dropping. The bit rate of the watermark is low, and it is not robust to
Besides, the embedded data rate is high and robust. The frame loss [6].
experimental results show that the embedded watermark is
robust and invisible. The watermark was successfully extracted Numerous video watermarking approaches suggested various
from the video after various attacks. ways of handling geometric attacks and they can be
classified into several categories: invariant watermark [7], [8]
Keywords-Video watermarking, geometric attacks, copyright synchronization [9], and autocorrelation [10].
protection.
Invariant watermarking embeds the watermark in a
geometric-invariant transform, such as a log-polar wavelet
transform, eliminating the need to identify and reverse the
I. INTRODUCTION specific geometric attacks, such as rotation, and scaling.
Digital watermarking has recently become a popular area These kinds of techniques are very weak against a slight
of research due to the proliferation of digital data (image, geometric distortion, such as small-angle rotation and near-
audio, or video) in the Internet and the necessity to find a one scaling. Moreover, the computational cost is too high to
way to protect the copyright of these materials. Visible obtain the invariant domain from the varied transform.
watermarks are visual patterns like logos, which are inserted The synchronization is the exhaustive search which entails
into the digital data. Most watermarking systems involve inversion of a large number of possible attacks and testing
marking imperceptible alteration on the cover data to convey for a watermark after each one. Since the number of possible
the hidden information. This is called the invisible attacks increases, the positive probability and computational
watermarks. Digital watermarks, on the other hand, are found cost become unacceptable.
with the advancement of the Internet and the ambiguity of
The autocorrelation technique is similar to the
digital data. Thus, it is natural to extend the idea of
synchronization approach. It spreads lots of extra data, in
watermarking into the digital data. Recently, numerous
addition to the real watermark information to obtain
digital watermarking algorithms have been developed to help synchronization for the watermark detection by
protect the copyright of digital images and to verify the autocorrelation, which either further distorts the host media
multimedia data integrity [1]. In spite of the existence of or sacrifices the watermark payload.
watermarking technique(s) for all kinds of digital data, most
of the literatures address the watermarking of the still images Chan et al, presented a novel DWT-based video
for copyright protection and only some are extended to the watermarking scheme with scrambled watermark and error
temporal domain for the video watermarking [2],[ 3]. correcting code [11]. The scheme is robust against attacks
such as frame dropping, frame averaging, and statistical
In this paper, we propose an oblivious video watermarking analysis. Campisi et al proposed the perceptual mask,
technique based on the spatial domain which is robust applied in the 3D DWT domain and robust against MPEG2
against geometric attacks, Besides, the embedded data rate is and MPEG-4 compression, collusion and transcoding attacks
high and robust. This paper is organized as follows: Section [12].
2 describes the related work; section 3 describes the
proposed algorithm. Section 4 describes the performance Elbasi proposed a robust mpeg video watermarking in
evaluation. wavelet domain which is embedded in two bands (LL and

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HH) and chosen attacks, JPEG compression, resizing, adding
Gaussian noise and low pass filtering [13].
Watermark
Anqiang presented adaptive watermarking scheme based on Modulation
the error correction code and Human Visual System (HVS)
in 3D-DWT domain. The proposed method is to resist the
signal processing attacks, Gaussian noise, and frame
dropping [14].
Xu Da-Wen proposed a method based on the 3D wavelet
transforms. In this method, the original video frames are Watermark Frame Dropping
divided into 3D-blocks according to the HVS properties. The embedding
proposed method is robust against lossy compression; frame
swapping, frame dropping and median filtering [15].
Al-Taweel and Sumari proposed video watermarking
technique based on the DWT based on the spread spectrum
communication. The proposed method is robust against
JPEG compression, geometric attacks such as Downscaling, Watermark
Cropping, and Rotation, as well as noising [16].
Extraction
Al-Taweel and Sumari proposed video watermarking
technique based on the discrete cosine transform domain Figure 1. Model of watermarking algorithm
based on the spread spectrum communication. The proposed
method is robust against JPEG compression, geometric More details about these four main steps can be found in the
attacks such as downscaling, cropping, and Rotation, as well next sections.
as noising such as guaussian noise and salt & pepper noise
[17]. A. Watermark Modulation

Al-Taweel and Sumari proposed a novel DWT-based video The watermark L= [11,12,….,1N] with li  {0,1}, is a bit
watermarking algorithm is proposed based on a three-level sequence of length N, which may be a meaningful image,
DWT using Haar filter which is robust against geometric like a logo of images of an owner.
distortions such as Downscaling, Cropping, and Rotation. It The watermark is modulated by a bit-wise logical XOR
is also robust against Image processing attacks such as low operation, that contains a pseudo-random bit sequence s =
pass filtering (LPF), Median filtering, and Weiner filtering. [s1, s2, …., sN] with si {0,1} which is than multiplied by
Furthermore, the algorithm is robust against Noise attacks another pseudo-number sequence (0,1) to provide the
such as Gaussian noise, Salt and Pepper attacks [18]. modulated watermark sequence W = [w1, w2, …, wN], as
Essaouabi and Ibnelhaj presented video watermarking shown in Figure (2).
algorithm in the three-dimensional wavelet transform. The The seed values of the two pseudo-random number
proposed algorithm is robust against the attacks of frame generators are regarded as the two private keys for the
dropping, averaging and swapping [19]. proposed algorithm.
The main significance of our technique is that it attempts to
Pseudo-random
realize a good compromise between robustness performance, Watermark
Bit sequence
quality of the embedding and computational cost.
III. THE PROPOSED ALGORITHM
In order to meet the requirements of invisibility and
robustness, an algorithm has been proposed that adaptively XOR
modifies the intensities of the host frames pixels, in such a
manner that it is unnoticeable to human eyes. The proposed
algorithm divides the host frame into a predefined number of
blocks; it also modifies the intensities of the pixels Pseudo-random
Number (0, 1) X
depending on the contents of the blocks. For security
requirements, private keys have also been used in this
algorithm.
In this section, the overview of proposed watermarking
Modulated
scheme is shown in figure (1). The scheme is composed of
four main components: watermark modulation, watermark watermark
embedding, frame dropping, and finally watermark
extraction. Figure 2. Modulation of the watermark

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Vol. 8, No. 2, 2010
On the other hand, if the embedded bit is 0, than the sum
B. Watermark Embedding Process of pixels in the watermarked block is smaller than that of the
original frame. Hence, the original and the watermarked
The modulated watermark bits are inserted into the host
frames are used in the extraction process. Both of the frames
frames blocks, depending on the contrast of the block. Before
are divided into the same blocks, which are used for the
the embedding process the host frame is decomposed into embedding process.
n×n blocks and the value of n is found as follows:
The sum of pixels for each corresponding block is
computed, and if the sum of the original frame block pixels
(1) is greater than that of the watermarked frame, the extracted
bit is considered to be 0, otherwise it is considered to be 1.
Where M, N and X, Y represent the dimensions of the host The extracted bits are then processed by XOR, with the
image and the watermark respectively. The process of same pseudo-random sequence used for embedding to
embedding in each block is carried out according to the produce the extracted watermark.
following procedure.
D. Watermarking Robust Against Frame Dropping
1- Splitting the video into frames I, B, P
2- Calculate the mean, maximum and minimum The effect of cropping and downscaling is similar for
values of the block. each frame, whereas the frame dropping is unequal on less
3- Find the values in the block that are above and significant frames from the scenes of the video. For the
below the mean value. embedded watermark to be robust against frame dropping, a
4- Calculate the mean values of those below the proposed method has been illustrated in Figure. (3), where
blocks mean value and the mean values of those the original video is segmented into scenes, then the digital
above it. watermark is divided into a number of blocks according to
5- Calculate the new pixels values V` according the number of scenes. The goal of dividing the watermark is
to the following: for embedding each block of watermark into its local scene
(for more details Figure (4) and Figure (5) illustrate the
 Inserted bit 0 embedding and extracting operations). Combining the
If V< mlow then technique mentioned in Section 3 will make the watermark
V’=Vmin robust against cropping, downscaling, rotation and frame
Else dropping.
If Vmean<V<mhigh then
V’=Vmean
Else
V’= V-a

 Inserted bit >0
If V<mhigh then
V’=Vmax
Else
If mlow< V < Vmean then
V’=Vmean
Else
V’= V+a

Where V is the original intensity, Vmean, Vmax, Vmin represent Figure 3. block diagram of proposed method for frame dropping
mean, maximum and minimum values of the blocks
1) Embedding Watermark
respectively. Whereas mlow and mhigh represent the mean
values of the pixels above and below the mean value of the As shown in figure 5.7 the steps of embedding watermark
block respectively. against frame dropping as follow:
a) Read watermark logo (modulated watermark).
6- Finally the original frame is replaced with the
b) Segment watermark data into no of blocks
resulting watermarked frame.
according to the number of scenes.
C. Watermark Extraction c) Embedded block no 1in the frames of the scene no
1.
According to the embedding procedure, the sum of pixels d) Embedded block no 2 in the frames of scene no 2
in the watermarked block is larger than that of the original e) Still embedded each block of watermark into its
frame if the embedded bit is 1. local scene.

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IV. PERFORMANCE EVALUATION
The proposed algorithm has been implemented in
MATLAB version 7.5 and the experiments have been
performed on a Pentium 4 PC running Windows XP. The
performance of the proposed video watermarking
algorithm has been evaluated on the basis of,
imperceptibility and robustness. The metrics were
evaluated using the standard video clips of 704×480 and
352x240 with size CIF and format 4.2.0 as shown in the
table (6.1). A 64×64 binary logo (USM) shown in Figure
Figure 4. essential operation of embedding each block of watermark in (6), will also be embedded into this. In fact, experimental
scenes of video.
results indicate that the algorithm is very robust to
2) Extracting Watermark geometric attacks. Figure (7) shows the original I-frame
for test clips, watermarked frame for test clips and
As shown in figure 5.8 the steps of extracting watermark extracted watermark.
against frame dropping as follow:
TABLE I. VIDEO CLIPS USED IN TESTING
a) Read watermarked video file.
b) Segmented the video into to the no of scenes. Video test sequence Size Format Frames Resolution
c) Extracting each block from any frame of local
scene. Susie on the phone CIF 4.2.0 450 352×240
d) Collect all extracted blocks. Flower garden CIF 4.2.0 150 352×240
e) Reconstruct watermark data.
Football CIF 4.2.0 150 704×480
Mobile and calendar CIF 4.2.0 450 704×480
Tempete CIF 4.2.0 149 352×288
Table Tennis CIF 4.2.0 150 352x240

Figure.6. original watermark logo
Figure 5. extracting algorithms

Figure 7. (a) original test frames of clips (b) watermarked test frames of clips (c) Extracted Watermark

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From figure 7 can see no difference between the resolution cropping, rotation, and frame dropping. Experimental results
of the original frame and watermarked frame. show that the proposed algorithm is very robust to geometric
Figure (8) shows the original watermark, extracted attacks; the similarity between the original and extracted
watermark without any threat with 1 correlation, and the watermarks is measured using the correlation factor a “NC”,
detection score respectively. it may take between 0 and 1.

  W. W *
NC  X Y
( 4)
 
X Y
W.W

(a) original watermark (b) Extracted watermark The similarity values vary in the interval [-1,1]; a value
well above 0 and close to 1 indicates that the extracted

sequence W matches the embedded sequence W. and
0.8
therefore, we can conclude that the video has been
watermarked with W.
0.6

1) Robust performance results against Downscaling
correlation

0.4

The watermarked frame is scaled down to 50% with the
0.2 aid of the bilinear interpolation method. Figures (9) show the
watermarked frame, extracted watermark, and its detection
0
score.
-0.2
100 200 300 400 500 600 700 800 900 1000
random watermarks

A. Imperceptibility Results

As a measurement for the quality of a watermarked (a)Watermarked frame (b) Extracted watermark
frame, the peak signal to noise ratio (PSNR) is used. PSNR is
defined as:

0.8

0.6
correlation

0.4

0.2

Where, X is the coefficients of the original video and X* 0
are the coefficients of the watermarked video. M and N are
the height and width of the frame respectively. In the -0.2
100 200 300 400 500 600 700 800 900 1000
proposed method, the watermark is embedded in the I-frame random watermarks

according to spatial domain. The average PSNR for all
watermarked frame is 37.72dB. With this PSNR value, no (c) Watermark detection results
quality degradation in the watermarked video is perceived. Figure 9. Watermarked frame under downscaling attack

B. Robustness Results 2) Robust performance results against frame dropping

Robustness is a measurement of the invulnerability of a The videos were segmented into seven scenes figure (10),
watermark against the attempts to remove or degrade it by assuming that seven watermarking groups were in need. The
different types of geometric attacks. For the proposed detection of the watermark after frame dropping of the
extracted watermark is shown in the Figure (11) and the
method, the video watermarking application robustness is
detection score has been shown in Figure (12).
measured against geometric attacks, such as downscaling,

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Figure 10. Frames on the scene boundaries of the video: Susie, tennis, , flower, and mobile

0.8

0.6

correlation
Figure 11: Decoded watermark under frame dropping attack 0.4

0.2

0.8
0

0.6
-0.2
100 200 300 400 500 600 700 800 900 1000
correlation

random watermarks
0.4

(c) Watermark detection results
0.2
Figure 13. Watermarked frame under cropping attack
0
4) Robust performance results against Rotation.
-0.2
100 200 300 400 500 600 700 800 900 1000 The watermark frame rotated by 5°,10°,15°,30° using
random watermarks
bilinear interpolation extracted logo with correlation of
Figure 12 .Random watermark detection results under frame
dropping attack
0.98, .97.97, 96 respectively, as shown in figure 14.
Figure 15 shows the watermarked frame rotated by –17°
3) Robust performance results against Cropping using bilinear interpolation, extracted logo with 0.99
Cropping approximately 50% of the watermarked frame correlation and detection score.
provides the covered watermark, although the correlation
value is relatively small, the recovered logo can easily be
distinguished, as shown in Figure (13).

(a)Rotated counter clockwise with 5° (b) Rotated counter clockwise with
10°

(a) Watermarked frame (b) Extracted watermark

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(c)Rotated counter clockwise with 15° (d) Rotated counter clockwise with proposed method.
30° ACKNOWLEDGMENT
Special thank and recognition go to my advisor, Associate
Professor. Dr. Putra Sumari, who guided me through this
study, inspired and motivated me.

Last but not least, the authors would like to thank the School
(e)Rotated clockwise with 5° (f) Rotated clockwise with 10° of Computer Science, Universiti Sains Malaysia (USM) for
supporting this study.
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Vol. 8, No. 2, 2010
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AUTHORS PROFILE

Sadik Ali M. Al-Taweel received the B.S. and
M.S degree in Computer Sciences from Al-
Mustansiriyah University and University of
Technology in 1991 and 2003, respectively.
During 2003-2005, he stayed at University of
Science and Technology Yemen as an instructor
of Computer Sciences and he worked as a
lecturer. Currently he is a PhD student at the
School of Computer Sciences, Universiti Sains
Malaysia. He is a member of IEICE and IEEE reviewer of International
Conference on Signal and Image Processing Applications (ICSIPA).

Putra Sumari obtained his MSc and PhD in
1997 and 2000 from Liverpool University,
England. Currently, he is a lecturer at the
School of Computer Science, Universiti Sains
Malaysia, Penang. He is the head of the
Multimedia Computing Research Group,
School of Computer Science, USM. He is a
member of ACM and IEEE, Program
Committee and reviewer of several
International Conference on Information and
Communication Technology (ICT), Committee of Malaysian ISO Standard
Working Group on Software Engineering Practice, Chairman of Industrial
Training Program School of Computer Science USM, Advisor of Master in
Multimedia Education Program, UPSI, Perak.

Saleh Ali K. Al-Omari Obtained his Bachelor
degree in Computer Science from Jerash
University, Jordan in 2004-2005 and Master
degree in Computer Science from Universiti
Sains Malaysia, Penang, Malaysia in 2007.
Currently, He is a PhD candidate at the School
of Computer Science, Universiti Sains
Malaysia. His main research area interest now
includes Peer to Peer Media Streaming, Video
on Demand over Ad Hoc Networks, MANETs,
and Multimedia Networking, Mobility.

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