c 4 steganography algorithm based on discrete...
TRANSCRIPT
72
CHAPTER 4
STEGANOGRAPHY ALGORITHM BASED ON
DISCRETE WAVELET TRANSFORM FOR
ROBUSTNESS AND SECURITY
In this chapter, we address steganography algorithm based on discrete wavelet
transform for robust and secure. Firstly, the previous work on Wavelet transform
(WT) will be reviewed. Then a brief discussion of Fourier transforms comparison
with Discrete Wavelet Transform (DWT), in which a DWT is a useful in signals and
image processing. To reach the upper bound of satisfaction for Robustness and
security, an optimal DWT domain steganography algorithm is proposed. The
advantage of DWT over the previous algorithms introduced in our proposed
4.1 INTRODUCTION
Image processing is a rapidly growing area of computer science. It’s started
growing fast in various technological advances such as digital imaging, computer
processors and in many storage devices. In the novel digital era, digital
communication and digital image are playing crucial role and visual information has
tremendous impact on human lives. It becomes an inseparable part of our day-to-day
activities. The digital image is one of the media of information, especially in the field
of information hiding.
Digital image processing is concerned with inserting and extracting useful
information from images. For instance, hiding and retrieve the information from
images. Ideally, this is done by computers, with little or no human intervention.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
73
“A picture is worth a thousand words.” In the modern era has introduced the standard
of digital information, especially in a digital image field associated with the benefits
and drawbacks.
The digital images in the field of steganography algorithm have a major difference
between spatial and frequency domain schemes is the convenience of implementation,
the two approaches can provide different functions to cope with various applications.
Generally, frequency or transform domains of steganography schemes tend to achieve
a better balance between robustness, security and fidelity than spatial domain.
Therefore, the embedded secret message procedures may have some image processing
technique. The image transforms coefficients; these coefficients have a large scale,
and perceptually important to the image representation. A large distortion on the
significant coefficients will result in serious quality degradation. Therefore,
embedding a secret message by slightly changing those significant coefficients will
result in a robust and secure steganography scheme because significant coefficients
usually remain stable even after image manipulation. The embedded of significant
coefficients can thus be detected more reliably. Therefore, the frequency domain
approaches are more popular in the steganography literature especially in DWT.
4.2 WAVELET TRANSFORM (WT)
The development of wavelets is a recently used in signal processing tool enabling
the analysis on several time scales of the local properties of complex signals that can
present non stationary zones [1-5].
A wave is a periodic oscillating disturbance that propagates through space and time,
usually with transference of energy. Wavelets are mathematical functions that divide
continuous time signal data into different frequency components.
A wavelet is a localized change of a sound signal in 1-D or localized variations of
detail in an image in 2-D.
The proliferations of wavelets are surprising in the scientific community,
academic as well as industrial. Wavelets lead to a massive number of applications in
various fields, such as geophysics, astrophysics, telecommunications, imagery and
information hiding, and a wide variety of signal processing tasks such as compression,
detecting edges, removing noise, and enhancing sound or images [6-12]. The
fundamental idea behind wavelets is to analyze according to scale.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
74
We use a process of scale to refine, understand and digest information. High scales
correlate with global information, and low scales correspond with the detailed
information. For instance, the large scale in image processing technique is the big
picture, while the small scale shows the details.
In the case of wavelets do not give the details about the time-frequency
representations, but gives details about the time-scale representations.
In various methods, the wavelet transform is similar to the Fourier transform. Where,
the wavelet transforms satisfy the mathematical measures to represent the input data
and uses the finite energy of the wavelet. However, the sinusoids to analyze signals
used by Fourier transform.
For every wavelet, there is a function ѱ called the mother wavelet described by
흍풋,풌(풙) = √ퟐ풋흍(ퟐ풋풙 − 풌),풋, 풌 = ퟎ,±ퟏ,… .. (4.1)
The mother wavelet recalls its name from the function she performs. She is the
prototype that generates the functions that are used in the wavelet transformation
process. She has children, grandchildren, great grandchildren, etc. She is only
constrained by the size of the original signal.
If the signal has 2n samples, she can give birth to n generations. For any mother ψ (x)
there is a function ϕ (x) called the scaling function (or sometimes called the father
wavelet). Its dilations are denoted by ϕ j, k. As the interval of the signal examined is
shifted, then the translation is the location of the interval, scale is the dilation [13, 14].
Various fields including mathematics, quantum physics, electrical engineering, and
seismic geology developed the concept of wavelets independently [15]. Therefore, it
is very difficult to credit everyone that has had an influence. Wavelets have been
outlined previously back to Alfred Haar in 1910 [16]. For many, the starting point of
their modern history coincides with two publications in the late 1980s by Ingrid
Daubechies [17, 18] and Stephane Mallat [19]. These innovators established a solid
mathematical foundation which would both explain and define the subject. Ingrid
Daubechies constructed the first orthogonal wavelet bases that were compactly
supported. Stephane Mallat is the father of Multiresolution analysis, which is the
foundation of contemporary wavelet philosophy. Mallat, a pioneer in the field,
established the idea that the wavelet transform, performed in a multi-scale manner, is
effective for analyzing the meaning of the content in images. A full background on
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
75
wavelets and a theoretical treatment of wavelet analysis given by Daubechies can be
seen in [19, 20] for wavelet history.
The real-world applications based on wavelets have a multiple disciplines. This
application shows the perfect practical such as, Image Processing in JPEG 2000 [21]
image compression standards, the U.S. Federal Bureau of Investigation (FBI)
fingerprint compression standard [22]. Human Vision used in detecting filopodia
edges [23, 24], JPEG 2000 [21] image compression standards. Geophysics studies in
Tropical Convection [25], the El Nino Southern Oscillation (ENSO) [26, 27] and
Central England Temperature [28]. Ocean Engineering used for the Analysis of
Underwater Sonar [29], Tide Forecasting [30] and Ship Roll [31]. Astronomy and
Astrophysics defined the Tidal Tails around stellar systems [32], determination of
coronal plasma [33] and the solar cycle complexity [34]. Seismic Geology as used in
earthquake prediction [35].
The idea behind why the wavelet is particularly suited to analyze time-frequency
information for each of these applications. While, the Fourier transform is not fitting
for its need.
4.3 FOURIER TRANSFORM
Fourier Transform (FT) was established around since the early 1800s to represent
a signal in the frequency domain. Joseph Fourier is a French mathematician; has
defined the Fourier Transform as a periodic function that stated as an infinite sum of
periodic complex exponential functions. The FT reveals how much each frequency
contributes to the signal. In signal processing, the FT is defined with the equation:
푿(풇) = ∫ 풙(풕)풆 풊ퟐ흅풇풕풅풕 (4.2)
Where t denotes time, f denotes the frequency of X (f) describes the signal in the
frequency domain. X (t) denotes as a signal in the time domain. The axis and
amplitude are holding the frequency on a graph. -∞ to +∞ denotes as an integration
bounds. For the transformation equation, the left hand side of the transformation
equation, X (f) is a function of frequency, and the integration is over time. Therefore,
the integral is calculated for every value of f.
Discrete Fourier Transform (DFT) is used when the function of an analog required
operating on distinct and sampled values of a signal with finite periodic. The DFT
computes the X k from X n. The equation for the DFT is:
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
76
푿풌 =ퟏ푵∑ 풙풏풆
ퟐ풙풊푵 풌풏풌 = ퟏ, ퟐ, …… .푵 − ퟏ푵 ퟏ
풏 ퟎ (4.3)
The DFT is a transform of finite-domain and discrete-time functions. Where, The Xk
denotes the amplitude and phase of the various components of the input signal xn. The
DFT’s values can be determined using a fast Fourier transform (FFT) algorithm. The
reverse process of FT is called as the inverse Fourier transforms, when a signal is
fixed into its constituent frequency elements, it can also be synthesized back into the
original signal.
푿(풇) = ∫ 풙(풕)풆풊ퟐ흅풇풕풅풕 (4.4)
The X (f), is the amplitude of the signal and phase, f (f), of the signal is demonstrated
in the superscript portion of e, if (f). The inverse DFT (IDFT) equation as:
푿풏 =ퟏ푵∑ 푿풌풆
ퟐ풙풊푵 풌풏풏 = ퟏ, ퟐ,…… . 푵 − ퟏ푵 ퟏ
풌 ퟎ (4.5)
The IDFT define the compute xn as a sum of components 푋 푒 with frequency
cycles per sample. The integration in the transformation equation is over time. The
left hand side is a function of frequency. Therefore, the integral is calculated for every
value of f.
FT can be a suitable tool to use if you are not interested in what times these frequency
components occur, but only interested in what frequency components exist.
There are many signals that are not stationary. An ECG (electrical activity of the
heart, electrocardiograph), an EEG (electrical activity of the brain,
electroencephalograph), and an EMG (electrical activity of the muscles,
electromyogram) are all examples of non-stationary signals. When the time
localization of the spectral component is needed, a transform giving the time-
frequency representation of the signal is needed. Wavelets do this and the short term
Fourier transform does this.
Short Term Fourier transform (STFT) is an FFT based method that provides time
and frequency localization to establish a local spectrum for any time instant. In the
STFT, the signal is divided into small enough segments, where these segments
(portions) of the signal can be assumed to be stationary.
Researchers approach has ended up with a revised version of the Fourier transform, as
short time Fourier transform (STFT). The equation follows:
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
77
푺푻푭푻푿(흎)(풕 , 풇) = ∫풕[풙(풕)흎
∗(풕 − 풕′)]풆 풋ퟐ흅풇풕풅풕 (4.6)
The FT multiplied by a windowing function ω(t), t denotes as constrained to a time
period. X (t) denotes as the signal itself, ω (t) denotes as a window function, and *
denotes the complex conjugation. For every t and f a new STFT coefficient is
computed. In the STFT if the window is of finite length, there is no longer perfect
frequency resolution. So, there are various kinds of window uses in the STFT, We
require to have a short window in which the signal is stationary. The narrower we
make the window, the better the time resolution, and the better the assumption of
stationary, but the poorer the frequency resolution.
Wide windows give good frequency information, but poor time resolution, plus the
signal may not be stationary within the wide window. If a good window function is
chosen the STFT may be an excellent choice for an application.
A narrow window means good time resolution, but poor frequency resolution. A wide
window means good frequency resolution, but poor time resolution.
The usefulness of a mathematical function lies in its efficiency and versatility in
representing various types of signals in the physical world.
The Wavelet Comparison of Fourier, Where a Wavelet transforms have
advantages over traditional Fourier transforms for representing functions that have
discontinuities and sharp peaks, and for accurately deconstructing and reconstructing
finite, non-periodic and/or non-stationary signals.
This problem in the past decades several solutions, has been developed which are
more able to represent a signal in the time and frequency domain at the same time.
The representation of a function by wavelets, the wavelet transform, overcomes this
deficiency. The wavelet transform, at high frequencies, gives good time resolution and
poor frequency resolution, while at low frequencies, the wavelet transform gives a
good frequency resolution and poor time resolution. So, we can gather all of the
information for the analysis of the signal, and decide which pieces that are needed.
Linear algorithms are similarities between the FT and the WT, fast Fourier transform
(FFT) and the Discrete Wavelet Transform (DWT).
In the FFT, the basis functions are sines and cosines. Unlike the Fourier transform,
wavelet transforms do not have a unique set of basis functions. Rather, the set of
possible basis functions for the wavelet transform is infinite. These basis functions are
called wavelets, or mother wavelets.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
78
Wavelets are localized in space, while Fourier transforms are not. Wavelet
analysis communicates knowledge that time-frequency methods such as Fourier
analysis cannot. Fourier series basis functions, which last the entire interval, give
frequency information. The wavelet basis functions give frequency information, but
are local in time. Long basis functions give detailed j, k frequency analysis. Short
basis functions are suited for signal discontinuities. Short high-frequency basis
functions combined with long low-frequency give both. This all-inclusiveness is what
wavelet transforms naturally provide.
FT Gives us the global picture, no local information, the FT of most real life
signals are not combinations of sinusoids, FT is sufficient for stationary signal
analysis and linear time invariant (LTI) systems, FT has Poor performance when the
frequency changes with time, FT have not a compact support compared to WT, an
interval is compact if it contains both of its endpoints. For example, the interval, often
written using square brackets, is compact because it contains 0 and 1 [36].
Therefore, wavelets have their energy concentrated in time and are well suited for the
analysis of transient, time-varying signals. Transient signals have finite duration and
are any physical phenomena that are not stationary. Three examples in which the
human sensory system concentrates on the transients rather than the stationary are: a
syllable pronounced as part of a spoken sentence, an edge located in an image, and
abnormal electrocardiogram (ECG / EKG) pattern in a heartbeat. Since most of the
real-life signals encountered are time varying in nature, the Wavelet
Transform fits many applications very well [37].
4.4 WAVELET TRANSFORM (WT) AND MULTI SCALE FUNCTIONALITY
One of the most fundamental problems in signal processing is to find a suitable
representation of the data that will facilitate an analysis procedure. One way to
achieve this goal is to use transformation or decomposition of the signal on a set of
basis functions prior to processing in the transform domain. Transform theory has
played a key role in image processing for a number of years, and it continues to be a
topic of interest in theoretical as well as applied work. Image transforms are used
widely in many image processing fields, including image enhancement, restoration,
encoding, and description. Wavelet transforms and other multi-scale analysis
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
79
functions have been used for compact signal and image representations in de- noising,
compression and feature detection processing problems for about twenty years. [35]
Multi scale is one of the most important features of wavelet transforms, [21], and
[35]. Physiological analogies have suggested that wavelet transforms is similar to low
level visual perception. From texture recognition, segmentation to image registration,
such multi-resolution analysis gives the possibility of investigating a particular
problem at various spatial-frequencies (scales). In many cases, a “coarse to fine”
procedure can be implemented to improve the computational efficiency and
robustness to data variations and noise. The transform coefficients had differing
statistics and perceptual importance. We use these differences to allocate bits for
encoding the different coefficients. This variable bit allocation resulted in a decrease
in the average number of bits required to encode the source output.
There are many mathematical transforms used to process the signal like FT
(Fourier Transform), STFT (Short Term Fourier Transform), DCT (Discrete Cosine
transform), Laplace Transform, Z Transform, Hilbert Transform and Wavelets etc.
[30, 31]. The theory of Wavelets starts with the concepts of Multiresolution Analysis
(MRA); the detailed theory of Multiresolution is found at [20, 21].
4.4.1 Continuous Wavelet Transform (CWT)
The Continuous Wavelet Transform was developed as an alternative approach to
overcome the resolution problem in the SFTF. A continuous wavelet transform is
used to divide a continuous-time function into wavelets. It is implemented in a similar
process as the SFTF: the signal is multiplied by a function, the mother wavelet instead
of a window function, and the transform is computed for individual portions of the
time-domain signal. However, the Fourier transform is not used and negative
frequencies are not computed, and the width of the window is modified for every
single piece of the spectrum. Diverse from the Fourier transform, the continuous
wavelet transform retains the way to construct a time-frequency, and make the
representation of a signal that proposals a good time and frequency localization.
The Continuous Wavelet Transform (CWT) is involution the sequence of input
data with a set of functions that generated through a mother wavelet. Usually, the
output of Xw (a, b) considers as a real valued function, but it is except when the
complex of mother wavelet is used. A complex mother wavelet method will transform
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
80
the continuous wavelet transform of a complex valued function. The power spectrum
of the continuous wavelet transform can be denoted by |X w (a, b) |2, for instance, The
CWT has Haar and Morlet wavelet that used to analyze a square wave signal. The
analysis and the synthesis of the CWT is also extremely computer intensive
comparing to DWT that has been a simpler and more efficient implementation.
In the engineering and computer science field are commonly used the technique of
Discrete Wavelet Transform (DWT), and in the scientific research the technique of
Continuous Wavelet Transform (CWT) is mostly used. For the purpose of
steganography an image, we want a non-redundant representation of the image.
Additionally, the original and stego images must be the same size.
4.4.2 The Discrete Wavelet Transform (DWT)
The discrete wavelet transform (DWT) is in literature commonly associated with
signal expansion into (bi-) orthogonal wavelet bases. We shall adopt the same
convention in this thesis. Thus, as opposed to the highly redundant CWT, there is no
redundancy in the DWT of a signal; the scale is sampled at dyadic steps푎 ∈
2 : 푗 ∈ 푍 , and the position is sampled proportionally to the scale푎 ∈ 푘2 : (푗, 푘) ∈
푍 . By no means can a DWT be understood as a simple sampling By no means can a
DWT be understood as a simple sampling we are dealing with finite-energy signal
푓(푥) ∈ 퐿 (푅) the wavelet 휓(푥) has to be chosen such as {휓(2 (푥 − 2 푘))}( , )∈
of L2 the first such basis was constructed by Alfred Haar in 1909, and the choice for
better ones has culminated in Ingrid Daubechies’s work. The Haar basis is an
orthogonal, scale-varying basis and is the simplest of all wavelets. What is an
orthogonal, scale-varying basis? A basis function is a flexible mathematical
description of data distributed over space and time.
The systematic framework for constructing wavelet bases was known as the
Multiresolution analysis. In [38-41], provide a comprehensive treatment of these
topics. A particularly comprehensive filter bank point of view is [42, 43]. The
orthogonal wavelets are rarely available as closed form expressions, but rather
obtained through a computational procedure which uses discrete filters. The link
between wavelets and these discrete filters is essential for understanding the Mallat’s
fast DWT algorithm in the previous section and its extension to images.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
81
The DWT [44] provides a number of powerful image processing algorithms. The
operation of steganography techniques of DWTs have become more attractive to the
information hiding community [45, 46].
The Discrete Wavelet Transform has a long history of showing its appropriateness
for information hiding applications. The secret message can be embedded in the
higher level frequencies, which are not as perceptible to the human eye, by reaching
the wavelet coefficients in the HL and LH detail sub-bands. However, not much
attention has been given to which wavelet may be the most successful for the original
image used especially in the reassemble of the embedded secret messages.
For instance, the 1-D wavelet transform can be easily extended to 2-D. The 2-D data
or photographs, scatter plots and geographical measurements. In the 2-D case, the
operation is used in an input matrix instead of an input vector. To transform the input
matrix, we first apply the 1-D wavelet transform on each row. Then, take the resultant
matrix, and then apply the 1-D wavelet transform on each column. This gives us the
final transformed matrix. The 2-D wavelet transform is used extensively in image
compression. For a 2-D transform, the filter can along with the rows, making two sub-
images to create each half of the original size. The presence of the heights look
similar as the original, but the sub-images have half the width. Then, sub-images used
to filter a low and high-pass along with the columns, this generates two more sub-
images each, for a total of four sub-images called as decomposition or analysis. We
mention the resulting of sub-images from an iteration of the DWT called an octave as
LL (the approximation), LH (horizontal details), HL (vertical details), and HH
(diagonal details), to generate the sub-image of the filters.
4.4.3 Wavelet Decomposition and Reconstruction
The various ways to compute the wavelet transform can be achieved by reiterating
for the average and differentiate coefficients of a couple of filter bank [42, 43]. The
low pass and high pass filter, where each of the filters is expected to be sampled by
two. Therefore, each of those two output signals can be further transformed. In the
same way, the process can be frequent reiterate several times, the resulting in a tree
structure called the decomposition tree. The process of decomposition in wavelet
transform is aimed used to decompose signals and image process through wavelet [22,
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
82
23]. Even the process of reconstruction is the reunited components back into the
original signal without loss of information.
In the figure shown below describe the Filter bank structure for one level
decomposition and reconstruction of two dimensional signals, and showing wavelet
decomposition at level 1, reconstruction, and four directions of image decomposition
and the decomposition of fruit image at 1-4 level.
Fig 4.1: Decomposition of wavelet at Level 1
Fig 4.2: Reconstruction of wavelet at Level 1
Fig 4.3: Representation Decomposition of fruits at level 1.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
83
Fig 4.4: Wavelet Decomposition at Level 1
Fig 4.5: Wavelet Decomposition at Level 2
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
84
Fig 4.6: Wavelet Decomposition at Level 3
Fig 4.7: Wavelet Decomposition at Level 4
The main structure of Wavelet can be represented as a four channel perfect
reconstruction of the filter bank. The types of filter (HPF or LPF) indicating each
filter that is 2D with subscript for separation of horizontal and vertical components
[47, 48]. The outputs of four-transform components result are involved of all possible
mixtures of high and low pass filtering in the two directions. These filters can be used
in one stage of an image to decompose into four bands as shown in the figures above.
The detail of images for each resolution can be classified as Diagonal (HH), Vertical
(LH) and Horizontal (HL). The operations can be repeated on the low (LL) [33].
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
85
4.4.4 Why the Discrete Wavelet Transform is needed?
The reason why the discrete wavelet transform is needed is depending according
to the application, applied to signals that based on mathematical transformations to
achieve additional data that is not available in the signal.
Wavelet transforms are used in a manner which divides a signal into those
components which are significant in time and space, and those that contribute less and
to be very useful in applications such as noise removal, edge detection and
information hiding.
In general, wavelets are useful when we try to obtain extra information from that
signal that is not available in the signal.
The signal transform is another method of representing the signal that will not modify
the information content available in the signal data.
Wavelets are contained of waves having energy focused on time or space, and it is
well suited for analyzing of transient signals. For instance, tide forecasting is
performed using wavelets. The ripples and trends in the ocean waters are transient,
and this is why wavelets were chosen.
The continuous wavelet transform (CWT) is a computation of the continuous
wavelet transform, where the wavelet series is the version used of the CWT. The
information used in it is offered the highly redundant to the extent that the
reconstruction of the signal is concerned. This type of redundancy needs a significant
of computation both time and resources. On the other hand, Discrete Wavelet
Transform (DWT) has abundant data from the original signal for both analysis and
synthesis, with an important reduction in the computation time. The DWT is easier
compare to CWT in the form of implementation.
For steganography, wavelets suitably and easily can break the image up into the
approximation and details. Wavelets will isolate and manipulated in high and low
resolution bandwidths, so that secret message can easily be embedded in the bands
which are less apparent to the human being eyes.
Multiresolution can analyze information embedded in an image with regards to time-
frequency content. It can be exploited to hide information in a way that humans will
not see. The information can be concealed in the details. The hidden information
should still remain robust because the approximation information is less likely to be
affected by image modification, such as compression or the addition of noise.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
86
Therefore, the wavelet transform has been successfully used to decompose complex
information and patterns into basic forms, having a track record and used several
other image processing applications.
4.4.5 Wavelet Domain for steganography Algorithms
The effective way to hide the secret data is depending on the choice of hiding
coefficients, i.e. on the transform domain used and the selection of the embedding
band. The steganography of wavelet transforms have been used in signal and image.
These include the global [49] and block based DCT [50], the DFT [51], the Fourier-
Mellin transform [52] and the fractal transform [53]. Other methods combine multiple
transforms. Some use the properties of the DFT and the DWT [54] while others utilize
the DCT and the DWT [55].
The steganography has an excellent description that is shown why wavelets are
well-suited for information hiding, especially on DWT [56].
The steganography approaches that use wavelets are classified in [56] as Algorithm
used to embed and extract the secret messages, Multiresolution strategy [57], Human
Visual System (HVS) modeling. The properties of the HVS are taken into account
either explicitly or implicitly, such as spatial masking, luminance masking and
contrast masking [58]. Other approaches use explicit perceptual measures called
contrast thresholds. Contrast thresholds, in general, refer to the minimum level of the
contrast that is observable by the human eye, Selection of the coefficients in the
embedding algorithm.
The best performance of wavelets in the information hiding is shown in [59, 60].
In [61] the embedding procedures in various images to obtain security and robustness,
even embed a secret message in the large coefficients of the high and middle
frequency details in wavelet by 2-level decomposition [62].
In [63] the system of embedding a 32-bit of the secret message in the image with
high redundancy, this idea of embedding is used to place one secret message at one
time in any iteration without repeating the same information.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
87
4.4.6 Advantages of DWT Steganography over the previous algorithms
Here some conditions why the DWT is suitable for steganography:
The DWT performs well for analyzing image features such as edges and
textures, especially in the space frequency localization.
Having the capability permits for the Embedder of secret message to reach to
the signal information when it may be used by other transforms, Such as
Multiresolution representation.
The wavelets transform used to break down an image into the approximation
HVS modeling for the robustness purpose.
The computational complexity of the DWT is O (n) used as Linear
complexity.
The wavelet transform is flexible and can be chosen according to the
properties of the image called as adaptively [66].
DWT has been existed to overcome the limitation of Short Time Fourier
Transform (STFT), which can be a useful for analyzing non-stationary signals.
The Wavelet Transform is useful when the multi-resolution technique
achieved by which the various frequencies are analyzed with different
resolutions.
It concentrated in time or space and suited to analysis of transient uses
wavelets of finite energy.
In Wavelet Transform, the sizes in the term of width that the function of
wavelet are changed with each spectral component.
Embedding is done by modifying least significant bits of selected wavelet
coefficients. Thus the number changeable coefficients in this case are equal to
the number of selectable coefficients.
It is an appropriate instrument for scrutinizing non-stationary signals that have
time varying spectra.
Wavelet transforms have advantages to cutouts and sharp peaks, and
analyzing, reconstructing finite, non-periodic and/or non-stationary signals.
It provides more information for analysis and synthesis, with an important
reduction in the computation time of the original signal and breaks the image
up into the approximation and details.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
88
4.5 STEGANOGRAPHY FOR ROBUSTNESS
The robust data hiding methods are also known as methods of the hidden data that
survive after various attacks. In this chapter, the data hiding term is referred for a
more general term including all hiding methods which can be robust or not. The
information hiding, especially in Steganography techniques has been studied in depth
than the non-robust techniques. These studies have been motivated by the emerging
needs of the combining text and image content providers which are looking for the
means to prevent the illegal distribution of their properties.
A passive adversary is the main difficulty that has evaded the steganography
solution that has multiplicity of the attacks. Such as random noise, friction or gravity;
but in the watermarking problem the adversary is a human being who can analyze the
system and better the attack strategy by time. The battle-of-wits situation between the
designer and the attacker for this problem puts the designer at a disadvantage because
of the multiplicity of attacks that needs to be accounted for. Steganography
researchers adopted some cryptographically techniques to discourage the attackers to
no avail. Some successful data hiding methods recover different attacks have been
proposed. The main ones of these methods are listed below:
Abdul-Jabbar et al. [64] in 2011, had worked with the effect of embedding domain
on the robustness in genetic watermarking proposed in frequency domain using
discrete wavelet transform (DWT), the robustness results for DWT are more than
DCT in the technique of watermarking that based on analyzing through Numerical
correlation. Th. Rupachandra et al. [65] in 2012, had given a proposed of an image
watermarking scheme based on visual cryptography in discrete wavelet transform that
practice various portions of a single watermark into various regions of the image. The
generate signals to be shared from the low frequency sub band of the original image
that's based on the binary watermark to study a global and local mean of DWT
technique. Manal and Thair [66] in 2011 proposed a technique that suggested
incorporating of double watermarks in a host image for improved protection and
robustness of wavelet domain in the second embedded data.
Chih-Chien et al. [67] in 2010, presents a novel of hiding secret text into the low
frequency of the overload component of a color image through the redundant discrete
wavelet transform. It gives a good result for imperceptibility and robustness. Po-Yueh
and Hung-Ju [68] in 2006, studies technique which embeds the secret messages in the
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
89
frequency domain where the algorithm is divided into two modes and 5 cases and
secret messages are embedded in the high frequency coefficients resulted from
Discrete Wavelet Transform. A. S. Imran et al. [69] in 2007, in their paper, discussion
a robust method that is encrypting the data and hide it that based on neighboring
pixels information” stated they can modify the Least Significant Bits technique for
data embedding by calculating the number bits that can be used for substitution based
on neighborhood pixel information.
Adaptive Methods are used in various from that a random embedding approach, it
adjusts the embedded signal to the local features of the original image. In [70] the
local characteristics of an image is first determined such as edge, uniform (non-edge)
with low/high intensity, moderately, very, extremely busy (high frequency terms).
The noise sensitivity of these classes is estimated and the signal is embedded
accordingly. In [71], the hidden signal is shaped to be masked by the original signal.
The computer simulations show that the throughput of the hiding system can be
improved with these techniques.
Invariance Methods are a novel approach to counter the expected attacks is to
insert the hidden signal into an invariant domain of the attack. If we desire the
invariancy to shifting, we can embed the hidden data to the amplitude of the Fourier
transform. The shift invariance idea is generalized to joint invariance under shift,
rotation and scaling in [72]. Due to the digitized nature of images, the domain
invariant to shift-scale-rotation operations turn out to be difficult to implement.
4.6 STEGANOGRAPHY FOR SECURITY
Privacy the indefeasible right of an individual to control the ways in which
personal information is obtained, processed, distributed, shared, and used by any other
entity" [73]. In addition, particular aspects of privacy lead to different but related
concepts, such as information privacy, communication privacy, bodily privacy, and
territorial privacy [74]. Anonymity technologies are more appropriate to guarantee
privacy [75]. Clearly, the items of interest in a steganographic system are the
messages themselves. Thus, steganography takes privacy in communications one step
further by making messages undetectable.
An ultimate goal in steganography is leading to both a secure steganographic
system and a private communication mechanism.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
90
Sherin et al, [76] in 2011, discussed how to embed secret messages into grayscale and
RGB Colored images. They applied a wavelet domain method of third level
decompositions to determine the positions and the magnitudes to adaptively embed
the secret message steganography for concealing a huge amount of data with high
security, good invisibility. Saddaf and Younus [77] in 2012 had studied algorithm to
hide text in any colored images of any size using wavelet transform for the purpose of
improvement of image quality, security and imperceptibility. Jila et al. [78] in 2011
used gray scale images for discrete wavelet transform technique, where the chaotic
maps are employed to generate a key space with the length of 1040 numbers to
increase the amount of security. Furthermore, the XOR operator has been replaced by
the transformation process for embedding processing of the images. Chin-Chen et al.
[79] in 2005, presents a scheme for embedding secret data into a binary image that use
a serial number matrix in the place of a random binary matrix to reduce computation
cost and provide higher security protection on hidden secret data.
Our critical survey and While going through the literature of the steganography
enhancement based on discrete wavelet transform, the embedding of DWT may be
achieved by quantization scheme, where the coefficients of the host data are modified
according to the bit of secret message [80 - 83]. Spread spectrum signal, where the
algorithm of secret data can be embedded by a sequence of numbers usually
pseudorandom Gaussian sequence [84, 85], or image fusion, where the logo image is
used as a secret message instead of a pseudorandom sequence [86]. We identified the
following research issues in the existing schemes: Most of issues of discrete wavelet
transform, we surveyed, were developed to satisfy robustness of image from a various
attacks. Only a few numbers of researchers were working on combining text and
image for the different layers of security and robustness. However, this technique still
being produced in a various ways to perform better algorithm of security and
robustness by text, ciphertext, biometrics data that can be hidden in the host image as
our proposed scheme shown.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
91
4.7 SUMMARY
An overview of the steganography algorithm based on discrete wavelet transform
with the purpose of robustness and security areas of interest. Hence it is focused in
this chapter the critical case study of Wavelet transform (WT), Fourier transforms,
Discrete Wavelet Transform (DWT), in which a DWT is a useful in signals and image
processing. It also reviewed the proposed of the Robustness and security, an optimal
DWT domain steganography algorithm. The survey of the approaches presented in the
literature to show the advantages of DWT over the previous algorithms are
introduced. Steganography enhancement based on combining text and image through
discrete wavelet transform will be described in our proposed approach in the next
chapter.
References: 1. Hong Wang Ling Lu Da-Shun Que Xun Luo, “Image compression based on wavelet
transform and vector quantization,” Machine Learning and Cybernetics, Proceedings.
2002 International Conference, Vol. 4, P.p. 1778 –1780.
2. M. Craizer, da Silva, E.A.B. E.G. Ramos, “Convergent algorithms for successive
approximation vector quantization with applications to wavelet image compression,”
Vision Image and Signal Processing, IEEE Proceedings, Vol. 146, P.p. 159 – 164.
3. Erjun Zhao Dan Liu, “Fractal Image Compression Methods: A Review”, Proceedings
of the IEEE Third International Conference on Information Technology and
Applications (ICITA'05) Vol. 2, P.p. 756 - 759.
4. Subhasis saha, “Image compression from DCT to wavelet: Review, ACM cross roads,
magazine 2000. http:/www.acm.org/crossraods/xrd6-3/saha image coding.html.
5. M. Ravasi, M. Mattavelli, D. J. Mlynek, A. Buttar, S. Soudagar, “Wavelet Image
Compression for Mobile for table Applications”, IEEE Intel. Conf. on Consumer
Electronics, 1999. ICCE’99, P.p. 374 - 375. 6. Tian-Hu Yu, Zhihai He, and Sanjit K.Mitra, “Simple and efficient wavelet Image
Compression”, IEEE Proceedings of Int. Conf. on Image Processing, Vancouver
Canada, 2000, P.p.174-177.
7. Sonja GrgC, Mislav GrgiC, Branka Zovko-Cihlar, “ Optlmal Decomposition For
Wavelet Image Compression”, IEEE Proceedings of the First International Workshop
on Image and Signal Processing and Analysis in conjunction with 22nd International
Conference on Information Technology Interfaces,2000. ISPA.2000, P.p. 203-208.
8. A. P. Beegan, L. R. Iyer, A. E. Bell,V. R. Maher, M. A. Ross, “Design And
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
92
Evaluation Of Perceptual Masks For Wavelet Image Compression”, IEEE ,P.p. 88-93.
9. Leonardo E. Urbano Sánchez, Héctor M. Pérez Meana, Mariko Nakano Miyatake,
“Wavelet Image Compression Using Universal Coefficients Matrix Detail
Estimation”, IEEE Proceedings of the 14th International Conference on Electronics,
Communications and Computers, P.p. 1-6.
10. Byung S. Kim, Sun K. Yoo, and Moon H. Lee, “Wavelet-Based Low-Delay Ecg
Compression Algorithm for Continuous Ecg Transmission”, IEEE Transactions on
Information Technology in Biomedicine Vol .10, No.1, January 2006, P.p. 77-83.
11. Chuo-Ling Chang, Xiaoqing Zhu, Prashant Ramanathan, and Bernd Girod, Fellow,
“Light Field Compression Using Disparity-Compensated Lifting And Shape
Adaptation”, IEEE, P.p. 793-806.
12. Alexandre Ciancio and Antonio Ortega, “A Dynamic Programming Approach To
Distortion-Energy Optimization For Distributed Wavelet Compression with
Applications To Data Gathering In wireless Sensor Networks”, IEEE Proceedings.
13. Syed Ali Raza Jafri, Shahab Baqai, “Robust Digital Watermarking For Wavelet-
Based Compression”, IEEE, P.p. 377-380.
14. Jun ZHANG, Yingjun HUANG, Hao TIAN, Lin LIAN,”Sar Image Compression
Based On Image Decomposing And Targets Extracting”, IEEE, P.p. 671-674.
15. Hanqiang Liu, Biao Hou, Shuang Wang, Licheng Jiao, “SAR Image Compression
UsingBandelets and Spilt”, IEEE, P.p. 1-4.
16. Nima Sarshar and Xiaolin Wu, “On Rate-Distortion Models For Natural Images And
Wavelet Coding Performance”, IEEE, Transactions on Image Processing, VOL. 16,
NO. 5, MAY 2007, IEEE 2007, P.p. 1383-1394.
17. WANG Aili, ZHANG Ye, GU Yanfeng, “SAR Image Compression Using
Multiwavelet and Soft-Thresholding”, P.p. 3536-3539.
18. Li Kun,Wang Shuang, Hou Biao and Jiao Licheng, “An Algorithm For SAR Image
Embedded Compression Based On Wavelet Transform”, Eighth ACIS International
Conference on Software Engineering, Artificial Intelligence, Networking, and
Parallel/Distributed Computing (SNPD 2007), IEEE 2007, P.p. 374-378.
19. G. A. Papakostas, Y. S. Boutalis, D. A. Karras and B. G. Mertzios, “Highly
Compressed Zernike Moments By SmoothinG”, IEEE P.p. 201-204.
20. Raghuveer M. Rao, Ajit S. Bopardikar "Wavelet Transform: Introduction to Theory
and applications", Second Edition, Addison Wesley publishing Company-2005.
21. S. G. Mallat, "A Theory for Multiresolution Signal Decomposition: A Wavelet
Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol. 11, No. 7, July 1989.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
93
22. Wavelet Toolbox users Guide, www.mathworks.com
23. R. Polikar, "The Wavelet Tutorial Part I: Fundamental Concepts and an Overview of
the Wavelet Theory, "http:// www.public.iastate.edu.
24. R. Polikar, "The Wavelet Tutorial Part II: Fundamentals: The Fourier Transform and
the Short Term Fourier Transform," http://www.public.iastate.edu/~rpolikar/
WAVELETS/WTpart2.html
25. R. Polikar, "The Wavelet Tutorial Part III: Multiresolution Analysis and the
Continuous Wavelet Transform," http://www.public.iastate.edu/~rpolikar/WAVELE
TS/WTpart3.html
26. R. Polikar, "The Wavelet Tutorial Part IV: Multiresolution Analysis: The Discrete
Wavelet Transform," http://www.public.iastate.edu/~rpolikar/ WAVELETS / WT
part4.html.
27. Yinpeng Jin, Elsa Angelini, and Andrew Laine, “Wavelets in Medical Image
Processing: De-noising, Segmentation, and Registration”, Department of Biomedical
Engineering, Columbia University, New York, NY, USA.
28. David Salomon, “Data Compression the Complete Reference”, Third Edition.
Springer-Verlag New York, Inc.
29. Khalid Sayood, “Introduction to Data Compression”, Second Edition, Morgan
Kaufman publisher. 2003.
30. R. C. Gonzalez, R. E. Woods, “Digital Image Processing” Second Edition, Pearson
Education, 2004.
31. A.K. Jain, “Fundamentals of Digital image processing” PHI, 2004.
32. P. Fränti, "Image Compression", Lecture Notes, 2002.
33. M. Nelson, Data Compression: The Complete Reference (2nd edition). Springer-
Verlag, New York, 2000.
34. James S. Walker, “Wavelet-based Image Compression Sub-chapter of CRC Press
book: Transforms and Data Compression”, Department of Mathematics University of
Wisconsin {Eau Claire Eau Claire, WI 54702-4004.
35. Michael B. Martin,” Applications of Multi wavelets to Image Compression”, Thesis
submitted to the Faculty of the Virginia Polytechnic Institute and State University
36. Konstantin Kozlov, Ekaterina Myasnikova, Maria Samsonova, John Reinitz, David
Kosman,“ Fast redundant dyadic wavelet transform in application” P.p. 459-462.
37. Daubechies, Ingrid, Different perspectives on wavelets: American Mathematical
Society Short Course, January 11-12, 1993, San Antonio, Texas.
38. Daubechies, Ingrid, Ten lectures on Wavelets, Society for Industrial and Applied
Mathematics.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
94
39. C. Mulcahy, "Image Compression Using the Haar Wavelet Transform.
40. C. Mulcahy, "Plotting and Scheming with Wavelet," Mathematics Magazine 69, 5,
1996.
41. G. Strang, "Wavelets and Dilation Equations: A Brief Introduction," Siam Review31,
1989.
42. V. Sterela, P.N. Hellar, G Strang, P Topiwala and C Heil, .The applications of
Multiwavelets filter banks to image processing, Preprint 1998.
43. V. Sterela, “A note on construction of biorthogonal multi-scaling functions,
Contemporary Mathematics,” 216:19-157, 1998.
44. M. R. Raghuveer and S. B. Ajit, Wavelet Transforms- Introduction to theory and
application, Addison-Wesley, 2000.
45. E. Elbasi and A. M. Eskicioglu, “A dwt-based robust semi-blind image watermarking
algorithm using two bands,” Proc. Symposium on Electronic Imaging, Security,
Steganogrphy, and watermarking of Multimedia Contents, PP. 777-787, 2006.
46. E. Ganic and A. M. Eskicioglu, “Robust DWT-SVD domain image watermarking:
embedding data in all frequencies,” Proc. ACM Multimedia and Security Workshop,
PP. 166-174, 2004.
47. J. Kovacevic and M. Vetterli, “Non separable Multidimensional perfect
reconstruction filter banks and wavelet bases for Rn,” IEEE Trans. on Information
Theory, vol. 38(2), pp. 533–535, Mar. 1992
48. Image Processing Toolbox for use with Matlab. The Mathworks Inc., Natick MA,
second edition, 1997.
49. I. J. Cox, J. Kilian, T. Leighton, and T. Shamoon, “Secure spread spectrum
watermarking for multimedia,” IEEE Transactions on Image Processing, vol. 6, no.
12, pp. 1613–1621, December1995.
50. E. Koch and J. Zhao, “Towards robust and hidden image copyright labeling,” in
Proceedings of 1995 IEEE Workshop on Nonlinear Signal and Image Processing,
Halkidiki, Greece, June 1995, pp. 452–455.
51. M. Ramkumar, A. N. Akansu, and A. A. Alatan, “Alatan, A robust data hiding
scheme for images using DFT,” in IEEE Proceedings of the International Conference
on Image Processing, 1999, pp. 211–215.
52. C. Lin and S. Chang, “Semi-fragile watermarking for authenticating JPEG visual
content,” in Proceedings SPIE International Conference on Security and
Watermarking of Multimedia Contents II, vol. 3971, January 2000, pp. 140–151.
53. J. L. Dugelay and S. Roche, “Fractal transform based large digital watermark
embedding and robust full blind extraction,” in ICMCS ’99: Proceedings of the IEEE
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
95
International Conference on Multimedia Computing and Systems, Washington, DC,
USA, 1999, p.p. 1003.124
54. J. Onishi and K. Matsui, “A method of watermarking with Multiresolution analysis
and pseudo noise sequences,” Systems and Computers in Japan, vol. 29, no. 5, pp.
11–19, 1998.
55. M. Corvi and G. Nicchiotti, “Wavelet based image watermarking for copyright
protection,” in The 10th Scandinavian Conference on Image Analysis (SCIA’97),
Lappeenranta, Finland, June 1997, pp. 157–163.
56. P. Meerwald and A. Uhl, “A survey of wavelet-domain watermarking algorithms,” in
Proceedings of SPIE, Electronic Imaging, Security and Watermarking of Multimedia
Contents III. SPIE, 2001, pp. 505–516.
57. R. Dugad, K. Ratakonda, and N. Ahuja, “A new wavelet-based scheme for
watermarking images,” in Proceedings of 1998 International Conference on Image
Processing (ICIP 1998), October 1998, pp. 4–7.
58. L. Xie and G. R. Arce, “Joint wavelet compression and authentication watermarking,”
in 1998 Proceedings of the IEEE International Conference on Image Processing -
ICIP, Kobe, Japan, October 1998, pp. 427–431.
59. W. Hsu, M. L. Lee, and K. G. Goh, “Image mining in IRIS: Integrated retinal
information system,” in ACM International Conference on Management of Data
(SIGMOD 2000), May 2000, pp. 593–593.
60. J. Z. Wang, G. Wiederhold, O. Firschein, and S. X. Wei, “Content-based image
indexing and searching using Daubechies’ wavelets,” Internation Journal on Digital
Libraries, vol. 1, no. 4, pp. 311–328, 1997.
61. K. Su, D. Kundur, and D. Hatzinakos, “Spatially localized image-dependent
watermarking for statistical invisibility and collusion resistance,” IEEE Transactions
on Multimedia, vol. 7, pp. 52–66, 2005.
62. X. G. Xia, C. G. Boncelet, and G. R. Arce, “A multiresolution watermark for digital
images,” in Proceedings of the IEEE International Conference on Image Processing
(ICIP 97), 1997, pp. 548–551.
63. J. Z. Wang and G. Wiederhold, “WaveMark: Digital image watermarking using
Daubechies wavelets and error correcting codes,” in Proceedings of the SPIE
International Symposium on Voice, Video, Data Communications, 1998.
64. A. Shaamala, S. M. Abdullah, A. A. Manaf, “The Effect of DCT and DWT Domains
on the Robustness of Genetic Watermarking,” Springer-Verlag Berlin Heidelberg, pp.
310–318, 2011.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
96
65. Th. R. Singh, Kh. M. Singh, S. Roy, “Image Watermarking Scheme based on Visual
Cryptography in Discrete Wavelet Transform,” IJCA, Vol. 39, No.1, Feb 2012.
66. Manal Khleifat, Thair Khdour, “A Proposed Mechanism for Securely Watermarking
Digital Images” International Journal of Research and Reviews in Computer Science,
Vol. 2, No. 3, June 2011.
67. Chih-Chien Wua, Yu Sub, Te-Ming Tuc, Chien-Ping Changa, Sheng-Yi Li,
“Saturation Adjustment Scheme of Blind Color Watermarking for Secret Text
Hiding,” JOURNAL OF MULTIMEDIA, VOL. 5, NO. 3, JUNE 2010.
68. Po-Yueh Chen and Hung-Ju Lin, “A DWT Based Approach for Image
Steganography,” Int. J. Appl. Sci. Eng, PP.275-290, vol.4, 3, 2006.
69. A. S. Imran, M. Y. Javed, and N. S. Khattak, “A Robust Method for Encrypted Data
Hiding Technique Based on Neighborhood Pixels Information,” World Academy of
Science, Engineering and Technology 31 2007.
70. B. TAO, and B. Dickinson, “Adaptive watermarking in the DCT domain,” Proc.
IEEE Int. Conf. Acoust. Speech Signal Process, P.P. 2985-2988, 1997.
71. C. Podilchuk, and W. Zeng, “Image-adaptive watermarking using visual models,”
IEEE Journal on Selected Areas in Comm., Vol. 16, PP. 525-539, 1998.
72. J. J. K. O’Ruanaidh, and T. Pun, “Rotation scale and translation invariant digital
image watermarking,” proc. IEEE Int. Conf. On image Processing (ICIP), Pp. 536-
539, 1997.
73. A. Acquisti, S. Gritzalis, C. Lambrinoudakis, and S. De Capitani di Vimercati,
editors. Digital Privacy: Theory, Technologies, and Practice. Auebarch Publications,
Boca Raton, FL, 2008.
74. PrivacyInternational.org. Privacy and Human Rights 2003: Overview.
http://www.privacyinternational.org/survey/phr2003/overview.htm.
75. Ian Goldberg. Digital Privacy: Theory, Technologies, and Practices, chapter Privacy-
Enhancing Technologies for the Internet III: Ten Years Later, pages 3–18. Auerbach
Publications, 2007.
76. Sherin Youssef1, A. Abu Elfarag, R. Raouf, “A Robust Steganography Model Using
Wavelet-Based Block-Partition Modification,” Intel Journal Comp Sci and IT, Vol 3,
No 4, August 2011.
77. Saddaf Rubab, M. Younus, “Improved Image Steganography Technique for Colored
Images using Wavelet Transform,” International Journal of Computer Applications
Vol. 39, No.14, Feb.2012.
STEGANOGRAPHY ALGORITHM BASED ON DISCRETE WAVELET TRANSFORM FOR ROBUSTNESS AND SECURITY
97
78. Jila Ayubi, Sh. Mohanna, F. Mohanna. M. Rezaei, “A Chaos Based Blind Digital
Image Watermarking in The Wavelet Transform Domain,” Int. J. Comp.Sci, Issue. 4,
Vol. 8, No 2, July 2011.
79. Chin-Chen Chang, Chun-Sen Tseng, and Chia-Chen Lin, “Hiding Data in Binary
Images,” Springer-Verlag Berlin Heidelberg, pp. 338 – 349, 2005
80. D. Kundur and D. Hatzinakos, "Digital Watermarking for Telltale Tamper Proofing
and Authentication,” IEEE Proceedings, pp. 1167-1180, 1999.
81. E. T. Lin and E. J. Delp, "A Review of Data Hiding in Digital Images," Image
Processing, Image Quality, Image Capture Systems Conference, pp. 274-278, 1999.
82. Y. Zhao, P. Campisi, and D. Kundur, "Dual Domain Watermarking for
Authentication and Compression of Cultural Heritage Images," IEEE Transactions on
Image Processing, vol. 13, pp. 430 - 448 2004.
83. M. Wu and B. Liu, "Watermarking For Image Authentication," IEEE in International
Conference on Image Processing, pp. 437-441, 1998.
84. N. Kaewkamnerd and K. R. Rao, "Wavelet based image adaptive watermarking
scheme," IEEE Electronics Letters, vol. 36, pp. 312- 313, 2000.
85. C.-s. Lu, S.-k. Huang, C.-j. Sze, and H.-y. M. Liao, "Cocktail Watermarking for
Digital Image Protection " IEEE Transactions on Multimedia vol. 2, pp. 209-224,
2000.
86. M. Jiansheng, , L. Sukang, , T. Xiaomei, “A Digital Watermarking Algorithm Based
On DCT and DWT,” In International Symposium on Web Information Systems and
Applications, WISA 2009.