CS 691, Summer 2009 – PROJECT REPORT
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A Study on Image Steganography
Archana Sapkota, Deepti Reddy

Abstract— In this paper we talk about what steganography is.
We go through the different existing steganomatic techniques and
methods. A lot of different steganography methods have been
proposed over a few years. We specifically discuss about the
existing JPEG steganography techniques. Though they have
achieved good visual resistance, many of them could not survive
the statistical attack. Several steganalytic methods have also been
proposed in past which detect existence of secret message in the
cover message. We review some of the steganalysis techniques
that break some JPEG steganography techniques.
Index Terms—steganalysis., Remote Monitoring, Wireless
Sensor Network.
I. INTRODUCTION
S
teganography is the art and science of writing hidden
messages in such a way that no one, apart from the sender
and intended recipient, suspects the existence of the message.
While going back to the history, in ancient Greece, people
wrote messages on the wood, then covered it with wax upon
which an innocent covering message was written. Herodotus
tells the story of a message tattooed on a slave's shaved head,
hidden by the growth of his hair, and exposed by shaving his
head again. The message allegedly carried a warning to Greece
about Persian invasion plans. This method has obvious
drawbacks such as delayed transmission while waiting for the
slave's hair to grow, and its one-off use since additional
messages requires additional slaves. In WWII, the French
Resistance sent some messages written on the backs of
couriers using invisible ink. During and after World War II,
espionage agents used photographically produced microdots to
send information back and forth. Microdots were typically
minute, about or less than the size of the period produced by a
typewriter. WWII microdots needed to be embedded in the
paper and covered with an adhesive. This was reflective and
thus detectable by viewing against glancing light. Alternative
techniques included inserting microdots into slits cut into the
edge of post cards.
Steganographic methods and various watermarking schemes
are applications of information hiding techniques.
Steganographic methods hide the secret information in the
cover carrier so that the existence of the embedded
information is undetectable. The cover carrier can be many
kinds of digital media such as text, image, audio, and video.
Because of the insensitivity of the human visual system, digital
images have been widely used as cover carriers in most
steganographic schemes, and are especially referred to as
image hiding techniques. Few applications of staganography
would be confidential communication and secret data storing,
protection of data alteration, access control system for digital
content distribution, media database systems etc.
Simmons formulated information hiding as a prisoners
problem. The same model can be adopted in the steganography
as discussed further in this paper. The adversary can be
considered active or passive. Alice and Bob are in jail, locked
up in separate cells far apart from each other, and wish to
devise an escape plan. They are allowed to communicate by
means of sending authenticated messages via trusted couriers,
provided they do not deal with escape plans. The couriers are
agents of the warden Eve (the adversary) and will leak all
communication to her. If Eve detects any sign of conspiracy,
she will thwart the escape plans by transferring both prisoners
to high-security cells from which nobody has ever escaped.
Alice and Bob are well aware of these facts, so that before
getting locked up, they have shared a secret codeword that
they are now going to exploit for adding a hidden meaning to
their seemingly innocent messages. Alice and Bob succeed if
they can exchange information allowing them to coordinate
their escape and Eve does not become suspicious.
Wendy
Is it stego?
Alice
Embedding
Algorithm
Cover message
Secret message
Secret key
Fig 1. Prisoner’s Problem
Bob
Extracting
Algorithm
Secret key
Hidden message
CS 691, Summer 2009 – PROJECT REPORT
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Substitution Techniques:
II. CLASSIFICATION AND TECHNIQUES USED IN
STEGANOGRAPHY
There are many approaches in classifying steganographic
systems. They can be classified according to the type of covers
used for secret communication. Here, we classify
steganography according to the cover modifications applied in
the embedding process. The six classifications are:
Substitution systems – Substitute redundant parts of a cover
with a secret message.
Transform domain techniques – Embed secret information in a
transform space of the signal (eg. Frequency domain)
Spread spectrum techniques – Adopt ideas from spread
spectrum communication
Statistical methods – Encode the information by modifying
many statistical properties of a cover and then use of
hypothesis testing in the extraction process.
Distortion techniques – Storing of information by signal
distortion and then measure the deviation from the original
cover in the decoding step
Cover generation method – Encode information in the way
that the a cover for secret communication is created.
There are three techniques to hide information in a cover file.
Injection (or insertion) – In this technique, data to be hidden is
stored in selections of a file that are ignored by the processing
application. This avoids modifying bits that are relevant to the
end-user. Binary files and executables are good file types for
injection. Taking advantage of unused or reserved space to
hold covert information provides a means of hiding
information without perceptually degrading the carrier. When
operating systems tore files, depending on the operating
system, some may end up having unused space that is allocated
to a file. This extra space can be used for embedding
information. Another method of hiding information in file
systems is to create a hidden partition. These partitions are not
seen if the system is started normally. However, in many cases,
running a disk configuration utility exposes the hidden
partition. Protocols in the OSI network model also have
vulnerabilities that can be used to hide information. TCP/IP
packets used to transport information across the Internet have
unused space in packet headers. Thousands of packets are
transmitted with each communication channel, which provides
an excellent covert communication channel if unchecked.
Substitution – Using this approach, the least significant bits of
information that determine the meaningful content of the
original file with is replaced with new data in a way that causes
the least amount of distortion. The main advantage of
substitution technique is that the cover file size does not
change
after
the
execution
of
the
algorithm.
Generation -. This technique doesn't require an existing cover
file. This technique generates a cover file for the sole purpose
of hiding the message.
In this paper, we look into various substitution methods used
to save data in image files.
Basic substitution techniques involve encoding secret
information by substitution insignificant parts of the cover
image by the secret message bits. In these methods, the
receiver can extract the secret information if he has knowledge
of the cover element positions used to hide the secret message.
Since only a few changes were made to the cover image, the
sender and receiver assume that a passive attacker won’t
suspect the cover image has a secret message embedded in it.
Least Significant bit substitution:
Using the least significant bit substitution, a surprising amount
of information can be hidden with little, if any, perceptible
impact to the carriers itself. The data can be easily
manipulated and recovered using steganography methods such
as the least significant bit substitution.
Embedding –For LSB, the substitution method involves
choosing a subset of cover elements and performing the
substitution operation on these cover elements. The substation
basically exchanges the LSB of the cover element 0 or 1. An
embedding process of more than 1 bit can be performed as
well. A 2-bit substitution can be achieved by storing two
message bits in the two most least significant bits of the cover
element.
Extracting – At the receivers end, the extraction process is
pretty simple and it involves extracting the LSB of the selected
cover-elements using during the embedding process and lining
them up to reconstruct the embedded or secret message. The
only piece of the information the receiver needs to know to
reconstruct the secret message is the sequence of cover
element indices used during the embedded process.
1
0
1
1
1
0
0
0
0
1
0
1
0
0
1
0
0
0
0
0
0
0
1
1
1
1
1
1
0
0
1
0
0
0
1
0
1
0
0
1
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
1
1
0
0
0
0
1
Figure 2: Hide letter “a” (ASCII code 91, in binary
01100001 inside eight bytes of the cover image
Approaches used for Embedding and Extracting to overcome
security threats:
One approach is where the sender simply uses all the cover
elements for embedding the secret information starting from
CS 691, Summer 2009 – PROJECT REPORT
the first cover element. The embedding processing using this
method will be completed long before the end of the cover
image and the unused cover elements will be left unchanged.
The problem with this method is that it poses a security risk as
the first part of the cover that was changed will be statistically
different in properties than the second part where the cover
elements were left untouched.
Algorithm 1.0 Embedding process using least significant bit
substitution
j, ---- jk(m) – sub-set of cover elements
m – message bit
Exchanges LSB of Cji by mi
For i = 1, ….., K(c) do
Si ← Ci
End For
For i = 1….., K(m) do
Compute index ji where to store ith message bit
Sji ← Cji
mi
End For
Algorithm 1.0 Extraction process using least significant bit
substitution
j, ---- jk(m) – sub-set of cover elements
For i = 1, ….., K(M) do
Compute index ji where to store ith message bit is stored
mi ← LSB(Cij)
End For
A better approach to overcome the security threat is to use a
pseudo random number generator that will help spread the
secret message over the entire cover in a random manner. One
common method used to achieve this is the random interval
method. In this method, the sender and receiver share a stegokey k that is used as a seed for a random number generator.
This helps to create a random sequence of indices that will be
used to embed the secret message. This way the distance
between two embedded bits is determined in a pseudorandom
way. The receiver in this method knows the seed k and has
knowledge of the pseudorandom number generator. This helps
the receiver to re-construct the indices used to embed the
secret message and in-turn get the secret image.
Pseudo-random collisions:
Considering all cover bits can be accessed in the embedding
process, the message bits can be distributed randomly in the
whole cover. This makes it hard for an attacker since there is
no guarantee that subsequent message bits are embedded in the
same order as the order here used is random.
Embedding: Alice (the sender) will create a sequence of
element indices and store each subsequent message bit in the
next index determined by the pseudo random number
generator. Considering the output of the pseudo random
number generator is not restricted in any way, particular index
3
could be used more than once. This causes Alice to use an
index more than once and corrupt the secret message bits.
These are called collisions. The probability of collisions
increases as the secret message is bigger and the cover image
is smaller.
Extracting : If the collisions are less, Alice can hope that the
corrupted bits are just a few and using an error-correcting
code, Bob (the receiver) can reconstruct the corrupted bits. As
the probability of collisions increases, this method is hard to
rely on. T
Approaches used to overcome risks: The probability of
collisions is negligible only for short images. One way to
overcome the risk of collisions is for the sender to keep track
of all cover bits that have already been used for substitution in
a set S. If an index is already contained in B, the sender will
simply discard the element number generated by the pseudo
random number generator and run the random number
generator to find an index that is not in set S. The receiver Bob
will have to use the same technique to be able to decode the
hidden message.
Palette-based images:
Uncompressed images like BMP provide a large space in
which one can embed messages. However, their redundant
data is obvious making it easily suspicious to steganalysts.
Hence, JPEG and GIF are better choices for steganographic
applications.
Steganographers tend not to like to use palette based images,
because the limitation on the colors available in a finite palette
causes difficulties in hiding data. Two approaches to
embedding messages in palette-based images are embedding
messages into the palette and embedding messages into image
data.
Embed images into Palette:
Shuffling Entries:One way this is done is to shuffle the color
entries and then use different combinations of color entries to
hide the intended message. Stego-image remains visibly intact
but the orders of the colors in the palette are changed. One
disadvantage of this method is that it is limited by the size of
the palette.
EZ Stego method :EZ stego method is similar to the commonly
used LSB method for 24 bit color images (or 8 bit grayscale
images). This is more widely used in which the colors in the
palette are first sorted by luminance, which is a linear
combination of three colors R, G, B in the palette. In the
palette that is re-ordered, most neighboring palette entries are
close to each other in the color space. The method embeds the
messages in a binary form into the LSB of indices (pixels)
pointing to the palette colors. Since, the colors with similar
luminance values may be relatively far from each other, this
method does not easily generate high quality steg-images.
Fridrich method: Hiding message bits into parity bit of close
colors-. The value of parity bit of the color R, G, B is
CS 691, Summer 2009 – PROJECT REPORT
determined. A message bit is embedded into each pixel of the
cover image. This is done by searching for the closest color
entry in the palette until a color entry with the desired parity
bit is found. The parity bits of the palette entries that
correspond to real images are more or less randomly
distributed, guaranteeing that the original colors are not
modified too much within the steg image.
Fig. 3. An example to illustrate the steganographicmethods. (a)
Original image (b) stego-image obtained
using EZ Stego method; (c) stego-image obtained using
Fridrich_s method;
4
International Standard-10918 (IS-10918). The JPEG
compression is based on the DCT and allows substantial
compression to be achieved while producing a reconstructed
image with high visual fidelity.
Following is the JPEG encoding procedure. The input image is
first divided into 8 × 8 non-overlapping blocks, and each block
is transformed by the FDCT into a set of 64 DCT coefficients.
These coefficients are then quantized using a quantization
table with 64 entries. The quantized results are all integers and
defined as the division of each DCT coefficient by its
corresponding quantization value, and rounding to the nearest
integer. The quantization step is lossy because of the rounding
error. The quantized coefficients are then passed to the entropy
encoding step to form the compressed code. One of two tables
must be provided in this step according to different entropy
coding schemes. If Huffman encoding is used, a Huffman table
must be provided. If arithmetic encoding is used, an arithmetic
coding conditioning table must be provided. It should be noted
that the entropy encoding step is lossless. The decoding
process consists of three steps: entropy decoding, dequantization and inverse DCT. Each step in decoding process
performs essentially the inverse of its corresponding step in
encoding procedure. The entropy decoding step decodes the
compressed code to the quantized DCT coefficients. The dequantization step then converts each quantized DCT
coefficient to its approximate value by multiplying with its
quantization value. DCT is then used to convert the dequantized coefficients to their spatial value.
III. JPEG STEGANOGRAPHY
Most image hiding systems use uncompressed images (e.g.,
BMP) or losslessly compressed images (e.g., GIF) as
coverimages. These images potentially contain much visual
redundancy so that they can provide large capacity to hide
secret data. Many image hiding systems have been proposed
and several stego-products have been developed based on
lossless image formats (e.g., EzStego). For reducing
transmission bandwidth and storing space, the JPEG image is
currently the most common format used on the internet. While
JPEG images have gained a lot of attraction, one of the
challenges in using JPEG images is the reduced embedding
capacity for steganography due to the efficient compression
technique using discrete cosine transform(DCT). But various
methods have been devices to make use of the maximum
capacity available in the images of such format. JPEG
steganographic methods use the DCT coefficients to embed
the secret bits.
A. JPEG compression
JPEG is an international standard for continuous-tone still
image compression which has been approved by International
Standard Organization (ISO) under the denomination of
Original
image
FDCT
Quantization
Entropy
Encoding
Compressed
code
Fig 2. JPEG encoding
Compressed
code
Entropy
Decoding
Fig 3. JPEG decoding
DeQuantization
IFDCT
Reconstructed
image
CS 691, Summer 2009 – PROJECT REPORT
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Fig 4. Histogram of JPEG coefficients after quantization
F5:
The main observations we get from the histogram of JPEG
coefficients is, the frequency of occurrence decreases with
increasing absolute value and the difference between two bars
in the middle is greater than the difference between two bars
near the end.
Instead of replacing the LSBs of the quantized DCT
coefficients with the secret bits, the absolute value of the
coefficient is decreased by 1. It also randomly chooses
randomly DCT coefficients to embed the secret bits.
Secret message
B. JPEG steganographic Algorithms
Quantization
A lot of JPEG staganographic algorithms have been proposed
in the past. In this section we will go through the proposed
algorithms. Most of the algorithms make use of DCT
coefficient to embed the hidden message. We briefly describe
some of the commonly used JPEG steganographic techniques
in this section.
J-steg:
Embeds the secret data by sequentially flipping the LSB of the
quantized DCT coefficients (except 0s and 1s) without causing
detectable artificial distortion. It has an embedding capacity of
12% and is secure against visual attack however fails easily
against statistical attacks. J-steg influences the pairs of the
frequency of the occurrence of the coefficients.
Fig 5. Message embedding in Jsteg
password
P
E
P-1
Pseudo random
Number generator
Entropy
Encoding
steganogram
Fig 7. F5 algorithm, P –permutation, E – embedding Function.
F5 is more statistically robust then Jsteg. The capacity is
claimed to be more than 13% and efficiency( number of bits
per change) is considered to be higher than Jsteg as well.
“Minimal distortion” embedding:
Each coefficient is assigned a scalar value expressing the
contribution of making an embedding change at that
coefficient to overall detectability. If the raw, uncompressed
cover image is available to the sender rather than just its JPEG
compressed form, the sender can use the knowledge of the
unquantized DCT coefficients to jointly minimize the overall
distortion due to quantization and embedding. This type of
embedding is called Perturbed Quantization and was also
utilized in the MMx-stego system. The guiding design
principle of these methods is the belief that the smallest the
embedding distortion, the harder it is to detect the embedding
changes. In other words, the methods are focusing on
increasing the embedding efficiency.
OutGuess:
Fig 6. Histogram of JPEG coefficients after Jsteg
The OutGuess steganographic algorithm was proposed by
Neils Provos10 to counter the statistical chi-square attack. In
the first pass, similar to J-Steg, OutGuess embeds message bits
along a random walk into the LSBs of coefficients while
skipping 0’s and 1’s. After embedding, the image is processed
again using a second pass. This time, corrections are made to
the coefficients that were not visited during the first pass to
make the stego image histogram match the cover image
histogram. Because OutGuess preserves the first order
statistics, the image histogram cannot be used as the
distinguishing statistics. On the other hand, even though the
global image histogram is preserved, the histograms of
individual DCT coefficients are not necessarily preserved.
They are only preserved if the shape of the individual
histogram is the same as the shape of the global histogram.
CS 691, Summer 2009 – PROJECT REPORT
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Spatial Domain JPEG steganography:
JPEG-compatibility steganalysis resistant method:
In recent work, Fridrich, Goljan and Du introduced [5] JPEG
compatibility” steganalysis method that is surprisingly accurate
at determining if bitmap images that originated as JPEG files
have been altered (and even specifying where and how they
were altered), even if only a single bit has been changed. And
Newman et al, described a steganographic method that
encodes the embedded data in the spatial domain, yet cannot
be detected by their steganalysis mechanism. Their method is a
hybrid in that it encodes the embedded data in the spatial
domain via JPEG coefficient manipulation; this is whyit is
resistant to detection using either spatial or frequency
steganalysis techniques. They claim that their topological
approach that may change many bits in the spatial file, but will
never be detected by JPEG compatibility steganalysis; it will
always produce a false negative.
YASS:
Both methods essentially embed data in the spatial domain in a
robust manner and then distribute the image as JPEG The
embedded message thus must be robust to JPEG compression,
which can be arranged using error correction. YASS[6] has
been shown to be undetectable using current best blind
steganalysis classifiers with payload of approximately 0.05 bits
per non-zero DCT coefficient.
Fig 8. Chi-square attack on Jsteg
Histogram Based attack on F5:
Though F5 algorithm is quite resistant towards chi-square
attack because it does not impact the overall histogram
distribution, but it does increase the number of zeros in the
histogram. So, the steganogram can be detected by observing
the difference between the histogram of the stegangram and
the cover image. Fridrich at el, have introduced a method for
estimating the cover image histogram. They have also given
the technique to estimate the unknown message length.
IV. JPEG STEGANOLYSIS
Chi- square:
If the bits used for overwriting the least significant bits are
equally distributed, the frequencies of both values of each
PoV(Pair of Values) become equal. The chi-square attack
explores the problem of PoV(Pair ov Values) to detect the
secret bits embedded in the LSB of the stego-image. The
detection of the chi-square attack is achieved by sequentially
accumulating the similarity of PoVs. That is, if the secret bits
are embedded in the LSB of the stego-image, they could be
detected by the chi-square attack. Different from the chisquare attack, the extended chi-square attack divides the stegoimage into blocks, and detects the secret bits by calculating the
similarity of PoVs in each block. If the chi-square attack is a
global method, the extended chi-square attack can be
considered as a local method because it detects the secret bits
in each block of the stego-image independently
As shown is the fig 6, the histogram of the DCT coefficient is
altered noticeably. Thus, Chi-square can easily break Jsteg
algorithm
Fig 9. Attack on F5
Re-embedding Technique attack on outguess:
OutGuess works by overwriting the LSBs. Embedding another
message into the stego image will partially cancel out and will
thus have a different effect on the stego image than on the
cover image. The embedding process introduces noise into the
DCT coefficients and thus increases the discontinuities in the
spatial domain along the boundaries of 8×8 JPEG blocks.
However, due to partial canceling of repeated LSB embedding,
CS 691, Summer 2009 – PROJECT REPORT
this increase in spatial discontinuities will be smaller when we
re-embed message bits in the stego image than the increase for
the cover image. This increase is a candidate for the
distinguishing statistics. Fridrich at el devised this technique to
break outguess.
7
conducted research on the filed of securing steganography.
Christian Cachin in his work on information theoretical model
for steganography gave the definitions of perfectly secure
steganography. He used the concept of relative entropy or
discrimination between two probability distribution to define
the secure steganography.
A stegnosystem is called perfectly secure if
Fig 10. Attack on outguess
JPEG Compatibility Steganalysis:
Fridrich, Goljan and Du introduced an ingenious steganalysis
technique that determines whether a bitmap representation of
an image derived from a JPEG file has been altered. If a
bitmap image were derived from an image once stored in
JPEG format, their method can determine this in most cases,
even if the low order bits of the image have been manipulated
after conversion to bitmap format. Their method takes
advantage of the last fact that not all spatial domain blocks can
be the output of decoded JPEG coefficient sets, i.e., not all
spatial blocks are JPEG blocks. Their steganalytical method
first determines that the bitmap was at one time stored in JPEG
format, then recovers the 8 × 8 JPEG block alignments and the
best candidate for the quantization table. It then detects those
blocks that could not have been produced by the JPEG
decoding process. Since changing a single bit in a spatial block
can cause a JPEG block to become JPEG incompatible, this
approach is extremely sensitive to manipulation of images in
the spatial domain; it can readily detect even low payload size
steganographical embeddings that do not take the JPEG
characteristics into account, provided they manipulate bitmaps
that were once stored in JPEG form.
V. SECURE STEGANOGRAPHIC MODELS
According to Kerckhoffs’principle the embedding algorithm is
supposed to be known to the public. Therefore, the embedding
process may use an embedding key so that only the legal user
can successfully extract the embedded data by using the
corresponding extraction key in the extraction process.
Securing the detection of the steganography is a challenging
task. A lot of techniques that have been proposed in the past
have failed the statistical attacks. At the same time people have
Where Pc is the probability distribution of the cover image
and Ps is the probability distribution of the stego image. He has
modeled the detection as a process of hypothesis testing. Phil
Sallee on his paper on Model-based steganography proposed
information-theoretic method for performing steganography
and steganalysis using a statistical model of the cover medium.
His method JPEG images which achieves a higher embedding
efficiency and message capacity while remaining secure
against first order statistical attacks. In their methods they
modify the least significant portions of the coefficients to
encode the hidden information. The model will consist of a
parametric description of the marginal DCT coefficient
densities. Lyu at el, in their work on detecting hidden message
using higher order statistics and support vector machine
proposed wavelet-like decomposition to build higher-order
statistical models of natural images. This model includes basic
coefficient statistics as well as error statistics from an optimal
linear predictor of coefficient magnitude. These higher-order
statistics appear to capture certain properties of \natural"
images, and more importantly, these statistics are significantly
altered when a message is embedded within an image. This
makes it possible to detect, with a reasonable degree of
accuracy. Thus modeling the steganograpy problem in the
higher order statistics makes it more secure to the future
attacks. Katzenbeisser and Petitcolas and by Hopper, et al.,
also proposed the definition to a secure steganography and
modeled steganography with the complexity-theoretic security
notions of modern cryptography, and to define a secure
stegosystem such that the stegotext is computationally
indistinguishable from the covertext.
VI. AVAILABLE TOOLS AND EXPERIMENTS
We tried to use some of the existing tools available in the
internet to see how some of the steganograms look like. We
picked fee available tools on random basis and used them.
Below shown is one of the tools used. It uses F5 like
algorithms and takes a cover file and the message as input and
embeds using the user given password. The generated
steganogram visually looks the same as the cover image. We
can also extract the embedded image using the same tools.
CS 691, Summer 2009 – PROJECT REPORT
8
(d)
(a)
(b)
(e )
Fig 11. (a)snapshot of the tool, (b) Cover image, summer 2009
white water rafting in Arkansas river. (c ) stego image with
embedded text. (d) Stego image with embedded image (e)
embedded image
VII. CONCLUSION
(c)
In this paper we gave on overview of different steganographic
mehods focusing on image steganography which have been
proposed in the past few years. Many simple methods have
attained good visual resistance but they have failed
significantly against the statistical methods. We also discussed
about the JPEG steganography which is one of the most
attractive methods today. The proposed JPEG steganography
and how they could not survive several statistical methods was
also discussed. We also review some steganalysis methods
which took advantage of the first order statistics to break the
steganograpy. Several secure models of steganography were
CS 691, Summer 2009 – PROJECT REPORT
also proposed in the past using information theory model and
also using higher order statistics. One of the biggest challenge
we saw while reviewing the work on steganography was the
definition of secure steganography. Most of the work in the
past was done considering the visual or the lower statistical
pattern of the image. It would be interesting to extend such
security models of steganography to a higher level or to do
further research on the possible patterns of the stego image and
the cover image. Extending the definition of perfectly secure
steganography to meet all the possibilities of detection would
be another interesting topic of research. An interesting future
research is to define a secure stegosystem such that the
stegotext is computationally indistinguishable from the
coverimage.
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