On the Security Enhancement of Multimedia Copyright
Protection for E-Business
M. A. Suhail
1
and M. S. Obaidat
2
1
University of Bradford, UK, ** Monmouth University, USA
2
Corresponding Author: Prof. M. S. Obaidat, Department of Computer Science, Monmouth
University, W. Long Branch, NJ 07764, USA.
Abstract. An important factor that slows down the growth of multimedia
networked services is that authors, publishers and providers of multimedia data
are reluctant to allow the distribution of their documents in a networked
environment. This is due to the fact that it is easy to reproduce digital data in
their exact original form, which encourages copyright violation, data
misappropriation and abuse. Watermarking security enhancement is highly
required for multimedia copyright applications. This work enhances the security
of watermarking algorithm without affecting the robustness of the watermark
by implementing the wavelet filter parameterization (WPF). Our experimental
work shows that the watermarking algorithm based WPF robustness can
enhance the security of watermarking.
Keywords. E-Business, Security Enhancement, Multimedia Copyright,
Watermarking, Discrete Wavelet Transform, Wavelet Filter Parameterization.
1 Introduction
Information is becoming widely available via global networks. The advent of
multimedia is allowing different applications to mix sound, images, and video and to
interact with large amounts of information (e.g. in e-business, distance education, and
human-machine interface). The industry is investing to deliver audio, image and
video data in electronic form to customers, and broadcast televisions companies,
major corporations, and photo archivers are converting their archives from analogue
to digital form. This movement from traditional content, such as paper documents,
analog recordings, to digital media is due to the several advantages of digital media
over traditional media [1].
Moreover, modern electronic commerce (e-commerce) is a new activity that is the
direct result of a revolutionary information technology, digital data and the Internet.
E-commerce is defined as the conduct of business transactions and trading over a
common Information Systems (IS) platform such as the web or Internet. The amount
of information being offered to public access grows at an amazing rate with current
and new technologies. Schemes used in e-commerce are allowing new and more
efficient ways of carrying out existing business. These have impacted not only all
S. Obaidat M. and A. Suhail M. (2004).
On the Security Enhancement of Multimedia Copyright Protection for E-Business.
In Proceedings of the 1st International Workshop on Electronic Government and Commerce: Design, Modeling, Analysis and Security, pages 104-115
DOI: 10.5220/0001404901040115
Copyright
c
 SciTePress

commercial enterprises, but also social life. The e-commerce potential was developed
through the World Wide Web (www) in the 1990s. E-commerce can be divided into
E-Tailing, E-Operations and E- Fulfillment; all supported by an E-Strategy. E-
Fulfillment is an area within e-commerce, which still seems quite blurred. It
complements E-Tailing and E-Operations as it covers a range of post-retailing and
operational issues. The core of e-fulfillment is payment systems, copyright protection
of intellectual property, security (which includes privacy) and order management (i.e.
supply chain, distribution, etc). In essence, fulfillment is seen as the "fuel" to the
growth and development of e-commerce. The focus of this paper is copyright
protection of multimedia copyright projection for e-business. The paper starts by
introducing the problem of copyright protection of intellectual property in the digital
environment, and then it provides a discussion about digital watermarking as one
solution of this problem. A review on security enhancement is given in section 3. The
paper discusses the discrete wavelet domain (DWT) and its features. Then, it explains
how the wavelet mother can be parameterized and adapted in the work to enhance the
security of the watermarking process. Results of our experimental work of the
watermarking embedding process and the conclusions are given at the end of the
paper.
2 Copyright Protection of Digital Intellectual Property
An important factor that slows down the growth of multimedia networked services is
that authors, publishers and providers of multimedia data are reluctant to allow the
distribution of their documents in a networked environment. This is because of the
ease of reproducing digital data in their exact original form which makes copyright
violation, data misappropriation and abuse easy. These are basically problems of theft
and illegal distribution of intellectual property. Therefore, creators and distributors of
digital data are actively seeking reliable solutions to the problems associated with
copyright protection of multimedia data. Also, this ease of transmitting digital
multimedia content over the Internet, has raised questions about how these rights
apply in the new environment? How can digital intellectual property be made publicly
available while guaranteeing ownership of the intellectual rights by the rights-holder
and free access to information by the user?
The concept of digital watermarking arose while trying to solve problems related to
the copyright of intellectual property in digital media. It is used as a means to identify
the owner or distributor of digital data. Watermarking is the process of encoding
hidden copyright information since it is possible today to hide information messages
within digital audio, video, images and texts, by taking into account the limitations of
the human audio and visual systems.
40

3 Digital Watermarking
Digital watermarking is an efficient technique to protect intellectual property from
illegal copying. It provides a means of embedding a message in a piece of digital data
without destroying the value of the digital data. Digital watermarking techniques
embed a known message in a piece of digital data as a means of identifying the
rightful owner of the data. These techniques can be used on many types of digital data
including still imagery, movies, and music. This work focuses on digital
watermarking for images and in particular invisible watermarking. A digital
watermark is a signal permanently embedded into digital data (audio, images, video,
and text) that can be detected or extracted later by means of computing operations in
order to make assertions about the data. The watermark is hidden in the host data in
such a way that it is inseparable from the data and so that it is resistant to many
operations that may degrade the host document.
Digital Watermarking techniques derive from steganography, which means covered
writing (from the Greek words stegano or ''covered'' and graphos or ''to write'').
Steganography is the science of communicating information while hiding the
existence of the communication. The goal of steganography is to hide an information
message inside harmless messages in such a way that it is not possible even to detect
that there is a secret message present. Both steganography and watermarking belong
to a category of information hiding, but the objectives and conditions for the two
techniques are just the opposite. In watermarking, for example, the important
information is the "external" data (e.g. images, voices, etc.). The "internal" data (e.g.
watermark) is additional data for protecting the external data and to prove ownership.
In steganography, however, the external data (referred to as a vessel, container, or
dummy data) is not very important. It is just a carrier of the important information;
the internal data. A watermark is designed to permanently reside in the host data. If
the ownership of a digital work is in question, the information can be extracted to
completely characterize the owner.
Digital watermarking is an enabling technology for e-business strategies: conditional
and user specific access to services and resources. Digital watermarking offers several
advantages. The details of a good digital watermarking algorithm can be made public
knowledge. Digital watermarking provides the owner of a piece of digital data the
means to mark the data invisibly. The mark could be used to serialize a piece of data
as it is sold or used as a method to mark a valuable image. For example, this marking
allows an owner to safely post an image for viewing but legally provides an
embedded copyright to prohibit others from posting the same image. Watermarks and
attacks on watermarks are two sides of the same coin. The goal of both is to preserve
the value of the digital data. However, the goal of a watermark is to be robust enough
to resist attack but not at the expense of altering the value of the data being protected.
On the other hand, the goal of the attack is to remove the watermark without
destroying the value of the protected data. The contents of the image can be marked
without visible loss of value or dependence on specific formats. For example a bitmap
(BMP) image can be compressed to a JPEG image. The result is an image that
requires less storage space, but cannot be distinguished from the original. Generally, a
41

JPEG compression level of (70%) can be applied without humanly visible
degradation. This property of digital images allows insertion of additional data in the
image without altering the value of the image. The message is hidden in unused
“visual space” in the image and stays below the human visible threshold for the image
[1, 2].
4 Literature Review
Digital watermarking can be developed using spatial and frequency domain
transforms. In frequency domain, researchers are using discrete cosine transform
(DCT), Discrete wavelet transform (DWT), fractal, etc. However, many
watermarking techniques address the utilization of the feature of DWT for the
watermarking embedding algorithms. Also, more attention has been given in recent
years to the security of watermarking algorithms using the DWT [1-10]. Watermark
attackers are making use of their knowledge of watermarking algorithms to defeat
watermarks. Some algorithms do not protect the location of the watermark
information; therefore anyone with knowledge of these watermarking processes can
attack them easily. Such algorithms cannot guarantee security. There is a trade-off
between robustness and capacity versus security [3]. Fridrich presents in [4-5] a
model of key-dependent basis functions intended to protect a watermark from attacks.
His algorithm improves resistance to attacks by inserting the watermark information
into a secret transform domain [10]. However, this algorithm is not practical because
of its computational complexity. It has huge computational requirements producing
many orthogonal patterns of the size of the host image. Kundur [6] proposed another
watermarking algorithm, which protects the location where the watermark
information is embedded. However, the algorithm is not secure enough to protect the
location where the watermark information is embedded. Also, both of these security
techniques limit the robustness of the algorithm. Meerwald et al. [3] introduced secret
wavelet filters using parameterization to decompose the host image. They tested their
idea with different known algorithms. They did not propose a new watermarking
system; only experimented with this idea on existing watermarking systems.
In this paper, we describe the enhancement of the security of a watermarking
algorithm without affecting its robustness by implementing wavelet filter
parameterization (WPF). This protects the location of the embedded watermark
information. We also present the watermarking process based on the WPF. The
proposed watermarking system relies on discrete wavelet domain decomposition,
which allows the independent processing of the resulting components without
significant perceptible interaction between them because the image is separated into
bands. Experimental results presented here show that the robustness of the WPF-
based watermarking algorithm is sufficient for the watermark to be extracted.
42

5 The Proposed Security Enhancement Scheme
The decomposition of the signal into different frequency bands is simply obtained by
successive high-pass and low-pass filtering of the spatial domain signal. A half band
low-pass filter removes all frequencies that are greater than half the highest frequency
in the signal. The new proposed watermarking scheme in this paper builds secret
wavelet filters. This is achieved by decomposing the host signal using wavelet
parameterization filters (WPF) [7] and keeping the parameter values secret. The
location of the watermark is protected because of the secret wavelet transform
domain. The security of digital watermarking schemes operating in the transformed
domain is improved without affecting the robustness or the invisibility of the
watermark. Also, incorporating WPF in the algorithm does not add significant
computational overhead. This section describes how to construct secret wavelet filters
by building parameterized 2-channel perfect reconstruction quadrature mirror filter
banks. The relation between the quadrature mirror filters (QMFs) H(e
-j ω
) and G(e
-j ω
)
is given by:
)(*)(
)( xwjjwjw
eHeeG
+−−−
−= (1)
H* (.) denotes the complex conjugate of H(.). In reference [8], it is shown that the
sequence {h
k
} should satisfy the following conditions:
1. Normalized Condition:
2
=
∑
k
h (2)
2. Orthogonality Condition:
)(2
2
mhh
mkk
δ
=
∑
+
(3)
3. Vanishing Moment Condition:
0)1( =−
∑
k
mk
hk , for m = 0, 1, …, M – 1 (4)
where M ≥ 1 and
δ (m) is a discrete delta function. Condition 2 indicates that
H(e
-jω
) and G(e
-jω
) are perfect reconstruction (PR) filters. This implies that the
following matrix:
⎥
⎦
⎤
⎢
⎣
⎡
+−−
+−−
=
−
Η
)
)(
()(
)
)(
()(
2
1
)(
πωω
πωω
ω
j
eG
j
eG
j
eH
j
eH
j
e
(5)
is unitary [9]. Therefore, H(e
-j ω
) and G (e
-j ω
) form a 2-channel perfect reconstruction
QMF bank [8]. H (e
-j ω
) has a zero of order M at ω = π when the third condition is
satisfied. This implies that the term (1 + e
-j ω
)
M
must be a factor of H (e
-j ω
) [7].
Defining Q (e
-j ω
) to be a polynomial in e
-j ω
, H (e
-j ω
) can be written in the form:
)()1()(
ωωω jMjj
eQeeH
−−−
+= (6).
43

The above Equations indicate also that G (e
-jω
) will have a zero of order M at ω = 0
when condition 3 is satisfied [8]. Therefore, to construct a compactly supported
orthogonal wavelet a sequence of scaling coefficients, h
k
must be built such that:
1. The matrix H(e
-jω
) is unitary i.e.:
)()(
1* ωω jjT
ee
−−−
= ΗΗ
2. H(e
-jω
) has a zero of order M at
ω = π.
Denoting the z transforms of the real sequences h
k
and g
k
(when z = e
-jω
) by H (z) and
G (z) [10],
∑
=
k
k
zhzH )( (7)
∑
=
k
k
zgzG )( (8)
The polyphase matrix is fundamental to many applications in multirate digital signal
processing. These include perfect-reconstruction analysis systems and efficient real
time implementation of decimation and interpolation filters. The polyphase matrix is
introduced here for the derivation of the WPF. The polyphase matrix E (z) is shown in
reference [9], to have the following parameterization:
0121
)(...)()()( VzVzVzVzE
NN −−
=
(9)
where:
⎥
⎦
⎤
⎢
⎣
⎡
−
=
00
00
0
cossin
sincos
θθ
θθ
V
(10)
]2,0[
0
π
θ
∈
T
kkk
vvzIzV )1()( −+=
for
11
−
≤≤ Nk (11)
where, vk is a 2 × 1 real vector with unit norm (
1=
k
T
k
vv ) and can be written in the
following form [3, 9]:
⎥
⎦
⎤
⎢
⎣
⎡
=
k
k
k
v
θ
θ
sin
cos
(12)
Then, the polyphase representation of the filters H (z) and z
2(N – 1)
G (z) based on E (z)
can be written as:
44

⎥
⎦
⎤
⎢
⎣
⎡
=
⎥
⎦
⎤
⎢
⎣
⎡
−
z
zE
zGz
zH
N
1
)(2
)(
)(
2
)1(2
(13)
It is shown in reference [9] that H (z) will be unitary if and only if the matrix E (z) is
unitary. The filter z
2(N – 1)
G (z) is used in (18) rather than the filter G (z) to guarantee
that the relation
k
k
k
hg
−
−=
1
)1( is satisfied. The filter H (e
-jω
) must vanish (have a
zero value) at some order
ω = π to be a scaling filter. When
ω =π, i.e., z = e
j π
= -1, then, I
z
T
k
v
k
vzI
z
z
k
V =
−=
−+=
−= 1
)1
2
(
1
)
2
(
(14)
Thus:
0
1
2
)( IVzE
z
=
−=
⎥
⎦
⎤
⎢
⎣
⎡
−
=
00
00
0
cossin
sincos
θθ
θθ
V
Then, for k = 1, 2, … , N – 1, equation (13) becomes:
⎥
⎦
⎤
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
−
+
=
−
=
−=
−
0
cos
0
sin
0
cos
0
sin
2
1
1
0
2
1
)(
)1(2
)(
θθ
θθ
V
z
zG
N
z
zH
(15)
To make the filter H (e
-jω
) have a zero value at some order ω = π , equation (15) must
be forced to be as shown below:
⎥
⎦
⎤
⎢
⎣
⎡
=
⎥
⎦
⎤
⎢
⎣
⎡
−=
−
2
0
)(
)(
1
)1(2
z
N
zGz
zH
(16)
Comparing equations (15 and 16),
,0cossin
00
=+
θ
θ
2cossin
00
=− θθ .
45

To satisfy these two equations with ]2,0[
0
π
θ
∈
,
4/3
0
π
θ
= .
This guarantees that H (e
-jω
) has at least a zero of order one at ω = π . Also, the wavelet
has at least one vanishing moment. In this work, the Daubechy 1 (Haar) wavelet is
used. The associated scaling function is given by a sequence of two coefficients {h
0
,
h
1
} where h
0
= h
1
= 1 [8]. Other wavelet functions can be used and even can produce
better results since the Daubechy 1 (Haar) wavelet suffers from discontinuous.
⎪
⎩
⎪
⎨
⎧
≤<−
≤≤
=
elsewhere
xif
xif
x
0
1
2
1
1
2
1
01
)(
φ
(17)
The parameterization of this wavelet is derived starting from equation (13). For a
sequence of two coefficients N = 2 and equation (13) becomes:
⎥
⎦
⎤
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
=
z
zE
zGz
zH
1
)
2
(2
)(
2
)(
(18)
Accordingly E (z
2
) [9] will be:
0
2
1
2
)()( VzVzE =
(19)
⎥
⎦
⎤
⎢
⎣
⎡
−
=
00
00
0
cossin
cossin
θθ
θθ
V
(20)
It has been proven [9] that
]2,0[
0
π
θ
∈
should be equal to 4/3
0
π
θ
=
, then,
⎥
⎦
⎤
⎢
⎣
⎡
−
=
11
11
2
1
0
V
Since N = 2 there are two parameters
θ
0
and θ
1
. With θ
0
= 3π/4:
⎥
⎦
⎤
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
−
+
−+=
z
z
T
vvzI
zGz
zH
1
1
]
11
)1
2
([
)(
2
)(
(21)
and,
46

⎥
⎦
⎤
⎢
⎣
⎡
=
1
2
11
11
1
2
11
sin
cossin
cossin
cos
γ
γγ
γγ
γ
T
vv
(22)
γ
1
is a parameter for wavelet filters. Substituting equation (22) in equation (21),
⎥
⎦
⎤
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
−
+
−+
=
z
z
zI
zGz
zH
1
1
]
1
2
sin
1
cos
1
sin
1
cos
1
sin
1
2
cos
)1
2
([
)(
2
)(
γ
γγ
γγ
γ
…………………………………………….(23)
Or
⎥
⎦
⎤
⎢
⎣
⎡
⎥
⎥
⎦
⎤
⎢
⎢
⎣
⎡
⎥
⎦
⎤
⎢
⎣
⎡
−
+
−+
−
−
−+
=
z
z
z
z
z
z
zGz
zH
1
1
1
2
sin)1
2
(1
1
cos
1
sin)1
2
(
1
cos
1
sin)1
2
(
1
2
cos)1
2
(1
)(
2
)(
γ
γγ
γγ
γ
…………………………………………….(24)
Therefore,
]
1
cos
1
sin
2
)1(
1
2
cos)1
2
(1)[1(
)(
γγγ zzz
zH
−−−++
=
………………………………………………(25)
Taking the inverse z transform [9] of (30):
⎟
⎠
⎞
⎜
⎝
⎛
−=
4
sinsin
110
π
γγ
h (26)
⎟
⎠
⎞
⎜
⎝
⎛
+=
4
sinsin
111
π
γγh
(27)
⎟
⎠
⎞
⎜
⎝
⎛
+=
4
sincos
112
π
γγh
(28)
47

⎟
⎠
⎞
⎜
⎝
⎛
−=
113
4
sincos
γ
π
γh (29)
In order to build secret wavelet filters, the parameter γ
1
should be kept secret. This
parameter is the key to the wavelet transform domain obtained when decomposing an
image using the associated wavelet filters. Keeping it secret ensures that the location
of the watermark, impressed on the transform coefficients, is also protected. The
parameterization of wavelet filter coefficients described above generates perfect
reconstruction filters.
6 Results
Experiments were carried out on images of sizes 256x256 and 512x512 to test the
proposed filter parameterization algorithm. The secret parameter γ
1
is chosen to be (-
0. 786534685492 rad) in this experiment and can be changed to any value between 0
and π rad. This value should be kept secret when building secret wavelet filters. The
image and watermark were both transformed using the parameterized DWT. The
impact of wavelet parameterization on the robustness and visibility of the resulting
watermark was then investigated. Experiments were performed with and without
parameterization for various kinds of attack. The impact of wavelet parameterization
on the watermarking robustness and invisibility were studied. An analysis was done
with and without parameterization against various kinds of attacks. Experimental
results obtained on different images. Sample results for the “fruit” image (Figure 1)
are shown in Figure 2 which is watermarked based on DWT-WPF. Figure 3 shows the
watermarked ‘fruit’ image attacked by JPEG compression. The analysis with and
without parameterization against JPEG can be seen in Figure 4. The watermark is still
easily recovered after using WPF since the correlation coefficient is well above the
threshold as can be seen in Figure 4. The threshold is determined based on empirical
studies and it is found to be 0.1. Hence, the security parametric filters are
implemented without perceptible image degradation. JPEG compression is applied on
different compression ratios, which varies from 5 to 50. Corresponding to that, the
correlation coefficient varies between 0.6 and 1. Figure 4 demonstrates the robustness
against compression attack that can be achieved with WPF even though there is a
tradeoff between security and the robustness.
7 Conclusion
This work improves the security of a watermarking algorithm without damaging the
robustness of the watermark. This is done by implementing wavelet filter
parameterization for use by the watermarking algorithm in the transform domain. The
location of embedded watermark information is protected by keeping the key for the
WPF secret. No one can extract the watermark without having the key for the WPF
filters as well as the key to generate the watermark. The experimental results show
48

some reduction of robustness when using WPF, but not enough to prevent the
watermark from being extracted. Adding the WPF to the DWT algorithm does not
cause much computational overhead.
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49

Appendix
Fig.1. Original Image.
Fig. 2. Watermarked Image.
Fig. 3. Compression of Watermarked image
using JPEG of CR 6:1.
5 10 15 20 25 30 35 40 45
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
JPEG Attack
Compression Ratio
Corrlation Coefficient
DWT
DWT-WPF
Fig. 4. Results of applying JPEG compression on the
watermarked image with and without parameterization
filters.
50