An Agent based Image Steganography using Information Theoretic ...

MASAUM Journal of Computing, Volume 1 Issue 2, September 2009 258
An Agent based Image Steganography using
Information Theoretic Parameters
Sattar B. Sadkhan, Abbas M. Al-Bakry, Naween N. Muhammad
Abstract- Steganography is the practice of hiding or
camouflaging secret data in an innocent looking
dummy container. Once the data has been embedded,
it may be transferred across insure lines or posted in a
public place.
Agent helps in steganography task, since the cover
image is important, so an agent subsystem makes
decisions that help in choosing the best cover image
from the image database.
This paper proposed a design and simulation of an
agent that helps in making decision by choosing the
suitable steganography method for the chosen cover
image (based on some information theoretic
parameters) to embed the specified secret image.
Index Term- Stego-image, Cover-image, Agent, secure
communication, Information theoretic parameters.
I. INTRODUCTION
Chin –Chen Chang et al [1] developed an optimal
least significant bit substitution in image hiding. The
processing of simple least significant bit (LSB)
substitution embeds the secret image in the least
significant bits of the pixels in the host image. The
processing may degrade the host image quality
significantly so that grabbers can detect that there is
something going on in the image that interest them. To
over come this drawback, an exhaustive LSB method that
uses a genetic algorithm to search approximate optimal
solution, and computation time is no longer so huge, can
be used . Experimental results show that the stego image
is visually indistinguishable from the original cover –
image [2].
• Tao Zhang and Xijian ping [3] designed an approach to
reliable detection of LSB steganography. A physical
quantity is derived from the transition coefficient between
different image histograms, and it’s processed version
produced by setting all bits in the LSB plane to
• zero. It appears that this quantity is a good measure of
the week correlation between successive bit planes, and
can be used to discriminate stego image from the coverimages.
Further studies indicate that there exists a
S. B. Sadkhan and Abbas M. Al-Bakry are with the, University of
Babylon, Iraq (phone: +9647801884154; e-mail: drengsattar@
ieee.org).
Naween N. Muhammad is with Al-Sulaimania University
functional relationship between this quantity and the
embedded message length.
• Najla A. Hamza [4] designed a robust technique for
information hiding in which image steganography is
proposed using Discrete Cosine Transform (DCT)
transformation technique. The proposed system depends
on substituting the similar blocks of the embedded image
with in a cover image. The system composed of number
of stages, transformation stage , matching stage ,
substitution stage and inverse transformation stage.
• A Parallel image processing system based on the
concept of reactive agents was developed in [18]. The
system described finely and simply the agent behaviors to
detect image feature. They proposed an approach for
continuity perception based on multi agent system. Each
agent can move around on its environment which consists
of an image made up of light and dark rings set out
concentrically. Agents are named darkening agent and
lightening agent. On the other hand, the higher level
communication requires that the objects (or agents) have
the ability to exchange message on an asynchronous way
with management of a message box in order to increase
the flexibility and liability of service demands,
proposition, and negotiation [19].
• Sabu M. Thampi and Chandra Sekaran [5] presented
an image retrieval system based on image feature,
steganography, and mobile agents. By utilizing the DCT
based information hiding technique, the valuable image
attribute can be hidden in an image with out degrading the
image quality. Mobile agents manage the query phase of
the system. Based on the simulation results, the proposed
system, not only show the efficiency in hiding the
attributes, but also provides other advantages such as :
i- Fast transmission of the retrieval image to the receiver
ii- No need to extract the attribute separately for other
application
iii- Searching made easy.
• Tabais S. G. G. [6] designed an agent based advising
systems that relieve the user of managing large set of
information, and helping to find a relevant objects in area
of interest. The performance for this product should be
possible to formalize. The agent is software component
that acts throughout representative and authority. Acting
representative means that should perform the tasks that we
wish it to perform, and which it can perform better or
faster than we can. This can for example be searching
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MASAUM Journal of Computing, Volume 1 Issue 2, September 2009 259
one thousand online store for best price for CD you want
considering.
This paper proposed a steganography system for based
on using agent software to make decision for the best
cover image existing in the available cover image
database that holds for the specific selected secret
message (image). The proposed agent depends on a
number of specific image parameters and some
measurement for the available cover images. Achieving
suitable steganography method, the client will obtain or
receive a better stego-image quality from the server.
This paper contains in addition to the first section the
following section:
Section 2 provides an overview on steganography and its
importance, while section 3 introduces briefly the concept
of agent system. Section 4 provides the proposed system
of steganography that based on the use of agent, while
section 5 gives the case studies on using the DCT
steganography and LSB steganography with the agent
system. Section 6 provides the conclusion.
II. STEGANOGRAPHY
Steganography and data embedding are increasingly
gaining importance in secure and robust communication
of vital information. The low sensitivity of human visual
system to luminance enable embedding large amount of
data in a still image or video , without causing any
discernible difference between the resulting signal or data
embedded signal , and original image [8].
There is now substantial body of literature on
steganography techniques, particularly in case when cover
medium is formed of digital images. An increasing
amount of steganalytic techniques for uncovering the
presence of steganography [9]. In conventional
cryptography, even if the information content are
protected by encryption, the existence of encrypted
communication is known. In view of this, steganography
provides an alternative approach in which it conceals even
the evidence of encrypted message. Generally,
steganography is defined as the art and science of
communicating in a cover fashion. It utilizes the typical
digit media such as text, image, audio, video and
multimedia as carrier, (which is called host or cover
signal) for hiding private information in such way that the
third parties (unauthorized person) cannot detect or even
notice the presence of communication [10] .
The goal of image steganogrohy is to embed information
in a cover image using modification that are undetectable,
in actual practice , however , most technique produce
stego images that are perceptually identical to the cover
images [11].
A. Steganography Types
Steganography divided into three main types, which are:
• Pure steganography.
• Secret key steganography.
• Public key steganography.
- Pure Steganography
A steganography system, which dose not requires a
prior exchange of some secret information (like a stegokey)
is called a pure steganography. Formally, the
embedding process can describe as a mapping
E:C×M→C. Where C is the set of possible covers, and M
the set of possible message. The extraction process
consists of a mapping D:C→M, to extract the secret
message out of a cover. Clearly it is necessary that
|C|≥|M|. Both sender and receiver must have access to the
embedding and the extraction algorithm, but the algorithm
should not be public [12].
- Secret key Steganography
A secret key steganography system is similar to a
symmetric cipher. The sender chooses a cover C and
embeds the secret message using a secret key. If the key K
used in the embedding process is known to the receiver,
he can reverse the process and extract the secret message.
Anyone who does not knows the secret key should not be
able to obtain evidence of the encoded information.
Formally, the embedding process is a mapping
E k :C×M×K→C and the extracting process is a mapping
D k :C×K→M where k is the set of all possible secret
keys.
- Public key Steganography
Public key steganography system requires the use of two
keys, one private and one public key. The public key is
stored in a public database.
One way to build a public key steganography system is
the use of a public key crypto system . Public key
steganography utilizes the fact that decoding function D in
a steganographic system can be applied to any cover C
(recall that D is a function on the entire set C). in the
latter case , there are a random element of M which will
be the results, which will be called the " natural
randomness " of the cover . If one assumes that this
natural randomness is statically indistinguishable from
cipher text produced by some public key cryptosystem a
secure steganography can be built by embedding cipher
text rather than unencrypted secrete message [4].
B. Classification Of Steganorgraphic Techniques
There are several approaches to the classification of
steganographic techniques. One of these approaches is to
categorize them according to the cover modification
applied in the embedding process. Mainly, steganographic
techniques may be grouped in to six categories as shown
in Fig. 1:
1- Substitution Technique: it substitutes redundant parts
of cover with secret message.
2- Transform Domain technique: technique embeds
secret information in a transform space of the signal (e.g.
in the frequency domain).
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MASAUM Journal of Computing, Volume 1 Issue 2, September 2009 260
3- Spread Spectrum Technique: It adopts ideas from
spread spectrum communication.
4- Statistical Technique: Encodes information by
several statistical properties of a cover and use hypothesis
testing in the extraction process.
5- Distortion Technique: It covers information by signal
distortion and measures the deviation from the original
cover in the decoding step.
6- Cover Generating Techniques: They encode
information in the way that a cover for secrete
communication is created.
- Substitution Technique
A basic and well known steganorgraphy technique for
hiding Message into uncompressed digital image is the
least significant bit (LSB) embedding methods. It is
considered as a well known example of this
steganographic technique. In this method , image pixels
are traversed in a predetermine – often pseudorandom –
order, and LSB for each pixels is modified to reflect the
corresponding bit in the message payload . LSB
embedding is preferred in many case for it's simplicity
and high embedding rate.
The process introduces a small amplitude noise signal,
and the resulting stego-image are visually identical to the
cover image [11].
The popularity of the (LSB) embedding is most likely
due to its simplicity as well as the [false] early belief that
modification of pixels value by 1 in randomly selected
pixels are undetectable because of the noise commonly
present in all digital images of natural scenes [12].
The LSB technique takes the advantage of random noise
present in the acquired media data, such as images, video,
and audio. Embedding message bits in the LSB plane will
not cause any discernible difference from original visual
[13].
The substitution modification technique is easy ways to
embed information, but they are highly vulnerable to even
small cover modification. An attacker can simply apply
signal processing technique in order to destroy the secret
information entirely.
- Transform Domain technique
It has been noted early in the development of
Steganography systems that embedding information in the
frequency domain of a signal can be much more robust
that embedding rule operating in the time domain. Most
robust Steganography system known today actually
operate in the same sort of transform domain [6].
Transform domain methods hide messages in the
significant area of the cover image which make them
more robust to attacks , such as adding noise ,
compression , filtering , cropping and some image
processing , than the substitution approach. However
while they are more robust to various kinds of signal
processing , they remain imperceptible . an example of the
used methods, is the discrete cosine transform (DCT) ,
and the wavelet transform. Transformation could be
applied over the entire image , to blocks throughout the
image . One popular method of encoding secret
information in the frequency domain is modulating the
relative size of two (or more) DCT coefficients within one
image block . During the encoding process, the sender
splits the cover image in (8x8) pixel blocks, each block
encodes exactly one secrete message bit . The embedding
process start with selecting a pseudorandom block bit
which will be used to code the ith message bit [6] .
- Discrete Cosine Transform
The discrete cosine transform (DCT) , like the Fast
Fourier transform (FFT) , uses sinusoidal basis function.
The difference is that the cosine transform basis are not
complex , they use only cosine functions . Assuming ( N
x N ) image , the DCT function is given by :
N−1
N
⎡ + ⎤ ⎡ + ⎤
= ∑ ∑
− 1
(2r
1) uπ
(2c
1) vπ
T(
u,
v)
α ( u),
α(
V)
I(
r,
c)cos⎢
⎥cos
(1)
⎢ ⎥
r= 0 c=
0 ⎣ 2N
⎦ ⎣ 2N
⎦
The inverse cosine transform is given by :
N−1
N
⎡ + ⎤ ⎡ + ⎤
= ∑ ∑
− 1
(2r
1) uπ
(2c
1) vπ
I(
r,
c)
α(
u),
α(
v)
T(
u,
v)cos⎢
⎥cos
(2)
⎢ ⎥
r= 0 c=
0
⎣ 2N
⎦ ⎣ 2N
⎦
Where α(u),α(v) = sqrt(1/N) if α(u),α(v) = 0
or
= sqrt(2/N) if α(u),α(v)= 1, 2, 3, .N
Where, sqrt(.), means the square root.
Given this interpretation of the DCT , the way to lose
the unimportant image information is to reduce the size
of the 64 numbers. There is a chance that this will nott
degrade the image quality much. This does not always
work , so in general , each of the 64 numbers is divided
by the different quantization coefficient (QC) in order to
reduce its size .
Quantization : after each 8×8 matrix of DCT
coefficient calculated it is quantized. This is the step
where information loss. Each numbers in the DCT
coefficient matrix is divided by the corresponding
numbers from particular quantization table used and the
results is rounded to the nearest integer .
A simple quantization table Q is computed , based on
one parameters R supplied by the user. A simple
expression such as
guarantees that Qs start
Q = 1+
( i + j)
× R
ij
small at the upper left corner and get bigger toward the
bottom right corner.
The (DCT) is applied not to the entire image but to the
data image units (blocks) to reduce the arithmetic
operation and then speed the algorithm up [7]. One can
remember that transform coefficient are the projection of
the Original image on to each basis image.
III. AGENT SYSTEM
It is fairly young area of research, there is not yet a
universal consensus definition of an agent. However, the
Wooldridge and Jennings definition:
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MASAUM Journal of Computing, Volume 1 Issue 2, September 2009 261
" As Agent is a computer system that is situated in
some environment and that is capable of autonomous
action in this environment in order to meet it design
objective".
Let us note that we are talking about (software agent)
whenever or any other researchers in the field say (agent),
we really mean software agent. The typical dictionary
definition of agent, as An entity having the authority to act
on behalf of another [14].
Some definitions were tied to specific implementation
technology such as being based on theorem proves , or
using internal data structure corresponding to the socalled
mentalist concepts , such as beliefs or knowledge ,
goal or desires , intention , and so on.
So a good working definition of agent is that [15]:
"It is a persistent computational entity that can
perceive, reason, Act, and communicate".
A. Software Agent Properties
The basic properties of software agent are that they are:
• Autonomous: being autonomous, mean that agents are
independent and make their own decision, this is one
property that distinguish agent from object.
• Situated ness : dose not constrain the notion of an agent
very much since virtually all software can be consider to
be situated in an environment [14].
• Flexibility : can be define to include the following
property :
I. Responsive: Refer to agent ability to perceive its
environment and respond in a timely fashion to change
that occur in it.
II. Pro-active : Agent are able to exhibit opportunistic ,
goal –driven behavior , take initiative where appropriate
III. Social: Agent should be able to interact, where
appropriate, with other agent or human in order to solve
their own problem and help other with their activities.
B. Agent Classification
The various definitions involve a host properties of an
agent. Having settled on a much less restrict definition of
an agent, this property may help us further classify agents
in useful ways.
Agent may be usefully classified according to the subset
of these properties that they enjoy. Every agent by our
definition, satisfies the first four property, adding other
property reduce potentially useful classes of agents. For
example: mobile, learning agent. Thus a hierarchical
classification based on set inclusion occurs naturally.
There are of course other possible classifying scheme ,
for example , we might classify software agent according
to the tasks they perform. For example, information
gathering agents or email filtering agent , or we might
classify them according to their control architecture. Then
would be fuzzy agent. Also agent could be classified by
the range and sensitivity of their senses , or by the much
internal state they posses [16].
IV. PROPOSED IMAGE STEGANORGRAPHY BASED ON
AGENT
This paper uses two methods of steganorgraphy, the
first one is the discrete cosine transform (DCT)
Steganography , and the second one is least significant bit
(LSB) steganorgraphy .
A. The Proposed Agent System in steganorgraphy
Our approach make general assumption about agent .
Also in this section we dedicate to present images
database which used by the agents. According to some
features of media (image) the agent will recommend the
user to select the best cover (image) and the suitable
image steganorgraphy methods that are assumed to be
available in this project for the selected secret image .
The Model of proposed steganorgraphy based agents
shown in Fig. 2, can be classified as: server agents, which
provides services to other. Since the Client- Server
connection is assumed to be used in this system, the
server (PC ) will provide the client with the STEGO.bmp
file ,which contains the secret image.
The basic role of agents in the system is to select image
from a database of images and make analyses of number
of image features , and according to these features, the
agent will choose the suitable steganography method and
best cover for the specific steganography method, and
give the user a recommendation for selected cover and
selected steganography system .
B. Agent system for Image feature calculation
In this system, the agent will place at random on
image (the environment). The choice of cover image is
important because it is significantly influences the result
obtained from the proposed system, and it will affect the
resulted security of the whole system.
The calculated image features by agent are:
• Histogram : is a plot of gray level values versus the
number of pixels. The histogram used as probability
distribution of gray level:
P (g) = N ( g)
(3)
M
Where M :is number of pixels in image .
N (g): is number of pixels at gray level g.
• Mean: is the average value, so it tells something
about general brightness of the image.
−
I( r,
c)
(4)
g = ∑∑
r c M
Where I(r,c) is the image pixel value.
• Standard deviation : which is known as square
root of variance. It tells something about contrast ,
and describes and expressed as follows:
L 1 −
=
σ ( g − g ) p(
g)
(5)
g
g∑ −
= 0
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MASAUM Journal of Computing, Volume 1 Issue 2, September 2009 262
• Entropy : the entropy is a measure for how many
bits we need to code the image data . As the pixels
values in the image are distributed among more
gray levels , the entropy increase [5]. And it can be
expressed as follows:
l 1
g∑ −
= 0
Entropy = - p(
g)log
[ p(
)]
2
g
Steganorgraphic agent computes the previous features,
after picking up an image from images database , and
make a decision or a choice. Detecting a suitable cover
for the secret image for embedding and specified
steganographic method. Steganographic agent tries to
find or detect an image with: highest variance ,
maximum contrast , and high entropy.
Agent chooses the cover image suitable for
steganographic method ; that is similar to secret image
and nearly of the same size of cover image.
(6)
Corr =
Where
1
⎡
⎢
⎣
N
N M
∑∑ ( I I
(9)
r=
1 c=
1
M
∑∑
I1(
r,
c)
− )( I
2
( r,
c)
− )
1
2
( I ( r,
c)
−
1
r= 1 c=
1
1
−
I
−
⎤ ⎡
⎥ ⎢
⎦ ⎣
N
M
∑∑
r= 1 c=
1
−
−
⎤
( I
2
( r,
c)
− I2
⎥
⎦
I − : is the mean value of original image
I ( r,
c)
that is :
1
−
1
=
1 M × N
N
M
I ∑∑ I
r = 1 c=
1
( r,
c)
1
I − : is the mean value of modified image ( , )
2
I r c
2
−
N M
1
I =
( r,
c)
2
2
M × N
∑∑I
r= 1 c=
1
(10)
that is :
(11)
If correlation is equal to 1, this means that the two images
are perfectly similar.
C. Evaluation parameters
There are two types of fidelity criteria; namely, the
objective and the subjective criteria. The first one
provides us with equation that can be used to measure the
amount of the errors in the reconstructed image. Where
the second criteria requires the definition of the
qualitative scale of image quality.
Commonly used objective measures are the peak signal
–to-noise ratio (PSNR), and correlation parameter [5].
1- Peak Signal –To-Noise Ratio (PSNR )
To describe the quality of the STEGO image precisely ,
we use the value of the (PSNR) to judge the similarity
between the host image and the STEGO image. The
(PSNR) function is defined as follows [20] :
PSNR = 10 2
( L − 1)
log db (7)
10
MSE
Where L = 1, 2…256
Here the MSE is the mean square error. Subsequently we
define MSE as Follow:
MSE= 1
( WH )
w
H
∑∑
i= 1 j=
1
( α ( I,
J ) − β ( I,
J ))
(8)
The symbol α ( I,
J ) and β ( I,
J ) separately represent
the pixels values of the cover image, and the STEGO
image in position (I,J) . While the W and H represent the
width and height of image respectively [18] .
2- Correlation (Corr)
This test measures the similarity between two images and
can be defined as follows [19]:
2
V. CASE STUDIES
The cover image (C) selected from image database by
steganography agent. The visual quality of STEGO
image is much better than that obtained when we select
cover image arbitrary from image database .
The image block size (taken into consideration in our
system ) is either (2,4,8), effects on the quality of stego ,
and best result ( maximum PSNR ) is obtained when
(n) or block size = 2 and with R = 0.1.
Although the block size chosen = 2 and the R = 0.1 , the
STEGO image has distortion.
We will test the effect of block size and the quantized
value R that effects the STEGO quality , and we chose R
value as small value, to get better STEGO image
quality .
Example :
Let us chose the secret image that we want to embed
inside the cover image . Then arbitrary selected a cover
image (C1.bmp) from image database Shown in Fig. 3.
The results of the objective test are as follows:
The Maximal value among all values of PSNR , it's
corresponding Stego image is most similar to the host
image (Cover image ). The block size = 2. And quantize
R value =0.1. And the feature of Cover image (C1.bmp)
or statistical feature is shown in table 1and 2.
VI. CONCLUSIONS
- The agent helps to identify component which offers
benefits.
- The agent makes decision. In this paper we have
analyzed different parameters by the agent. The
agent we have deal with have information about
cover image and can adapted to BMP type of cover
image.
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MASAUM Journal of Computing, Volume 1 Issue 2, September 2009 263
- The goal of steganography is to avoid attacker from
discovering the secret images embedded in the
cover image. To improve security level we must
obtain acceptable STEGO image quality. Overall,
the proposed system matches the requirements to
obtain acceptable STEGO image.
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TABLE 1. THE OBJECTIVE MEASURES FOR S1.BMP &
C1.BMP CHOSEN BY STEGANORGRAPHY AGENT
ACCORDING TO IMAGE FEATURE.
Block size Quantize value R PSNR(db) Correlation
2 0.1 30.5913 0.998366
4 0.1 25.1837 0.994315
8 0.1 22.2076 0.988698
TABLE 2. C1.BMP FEATURES
Mean 111.5858
Entropy 7.763618
Energy 5.252129e-
003
Variance 4918.591
Contrast 70.13267
Secret image ( S1.bmp) Cover image (C1.bmp )
STEGO Image
Fig. (3) The Secret image (s1), the cover image C1,
and the resulted stego image used in example.
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MJC010234 264
STEGANOGRAOHY
Substitution
system
Transform
Domain
technique
Spread
spectrum
technique
Statistical
method
Distortion
technique
Cover
generation
method
Fig. (1) Steganorgraphy Classification
Agent read cover
image &Extract
feature
Cover
Image
DB
DCT
Steganograph
STEGO
Image
Server Computer
Agent make a decision
for best cover image and
choose suitable
steganography method
LSB
Steganography
DCT
Steganograph
Dr. Eng Sattar Bader Sadkhan , Chief
Scientific Researcher Science 1997,
Technology Expert Since 2003, Chairman of
IEEE IRAQ SECTION and (currently)
Postgraduate Lecturer in Babylon University.
Expert in : Wireless Digital Communication
and Information Security.
Diploma in Radar Equipments Repairing (1970-
1974)- from Wireless and Radar training Center – B.Sc. (1974-1978)
Baghdad – IRAQ – Electrical and Electronic Engineering. M.Sc. (1979-1981)
in VAAZ Academy- BRNO – CZECH Republic –Wireless Communication
Engineering. Ph.D. (1981-1984) in VAAZ Academy- BRNO- CZECH
Republic- Detection of Digital Modulated Signals. Diploma in Cryptography
in 1988 – From Switzerland.
Since 1991 up to 2003, in Communication Research and Development Center
–Institute of Research and Development. I am engaged as Researcher in this
Center and I obtained the director position of this center in 2000 till 8 April
2003.
Postgraduate Lecturer, Director of Cultural Relation and Scientific Affairs,
Director of Research Centers of Babylon Studies,
Editor-in-Chief, for the International Scientific Journal of Advancement
in Computing Technology, (IJACT), in South Korea, since beginning of
2009.
http://www.aicit.org/ijact/
Member of (80) Scientific and Editing Committees of International
Scientific Journals, and International Conferences (Arabic and International).
Advisor of more than (100) Postgraduate theses (M.Sc. And Ph.D.).
Publishing more than (160) papers in (Arabic, and International)
Academicals journals and conferences.
Reconstructed
Reconstructed
image
image
Extraction
process
Extraction
process
Client Computer
STEG
O file STEG
O file
Fig. (2): The Agency of Stenography system Diagram
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