shodhganga - 2. literature review 2.1 introduction 2.2...
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Steganography of Text and Images Using Fractals Page 41
2. LITERATURE REVIEW
2.1 INTRODUCTION
In this chapter, we review different steganographic techniques like Hide and Seek , JSteg,
OutGuess 0.1, Out Guess 0.2, F3, F4 and F5and other related works regarding
steganography are presented. We also presented comparison of various steganography
techniques.
2.2 STEGANOGRAPHY TECHNIQUES
There are many techniques for steganography available and widely used among the
researchers in the field of computer science .Some of the popular techniques are
described below:
2.2.1 Hide and Seek
Steganography is applicable to all data objects that contain redundancy. People often
transmit digital pictures over email and other Internet communication, and JPEG is one of
the most common formats for images. Moreover, steganographic systems for the JPEG
format seem more interesting because the systems operate in a transform space and are
not affected by visual attacks [1]. Visual attacks mean that you can see steganographic messages on the low bit planes of an image because they overwrite visual structures; this
usually happens in BMP images. Neil F. Johnson and Sushil Jajodia [2] showed that
steganographic systems for palette-based images leave easily detected distortions.
In the encoding process of this technique can be implemented by taking the first pixel of
the image and obtaining its LSB value. This is typically achieved by calculating the
modulus 2 of the pixel value. This will return a 0 if the number is even and a 1 if the
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2. LITERATURE REVIEW
number is odd, which effectively tells us the LSB value. Then compare this value with
the message bit to be embedded. If they are already the same, then nothing to do, but if
they are different then replace it. This process continues until the values are encoded. While, in decoding process, Encoder replaced the LSBs of the pixel values in sequence;
the order is known to be used to retrieve the data. Therefore just calculate the modulus 2
of all the pixel values in the stegogrammes, and able to reconstruct original data.
2.2.2 JSteg
The JSteg algorithm was developed by Derek Upham and is essentially as same as a copy
of the Hide & Seek algorithm, because it employs sequential least significant bit
embedding. In fact, the JSteg algorithm only differs from the Hide & Seek algorithm because it embeds the message data within the LSBs of the DCT coefficients, rather than
its pixel values [3].
In encoding process, JSteg algorithm shows an alternative method for calculating the
LSB value of the coefficient by using mod 2. The result is replaced with the value .No
key is used for this algorithm. So long as the decoder knows that the embedding took
place in the DCT domain, it will be capable of extracting the message successfully. The
main difficulty of not using a key is when we try to determine LSBs when extracting the
message. Without a key, it is impossible to know the length of the message to extract, so
the loop is typically run for the entire duration of the image to ensure that the entire
message is extracted. Though, in the decoding process, the decoding process functions by
converting the stegogrammes to the DCT domain. It then avoids the same coefficient
values that the encoding algorithm avoids, and retrieves the hidden message from the
LSBs of all the other coefficients sequentially.
2.2.3 OutGuess 0.1 In much the same way that embedding the message data sequentially using the Hide &
Seek method was not considered very secure, neither was the fact that the JSteg
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algorithm embedded in the same fashion. The first version of Outguess, designed by
Neils Provos [4], improved the JSteg algorithm by scattering the embedding locations
over the entire image according to a PRNG on image. This is very similar to the way that
the randomized embedding approach improved the Hide & Seek algorithm. While
decoding process, for OutGuess 0.1 is might expected. Firstly, the stegogramme is
converted to the DCT domain, before being shuffled in the encoding process. Then
retrieve the message data by extracting the LSBs from all the coefficients whose values
are neither a DC value, nor a 0 or a 1.
2.2.4 OutGuess 0.2
OutGuess 0.1 algorithm is considered much more secure than the Hide & Seek and JSteg
algorithms because it not only embedded the information in a more discrete area of the
image (DCT coefficients), but it also scattered the locations of embedding by using a
PRNG to shuffle the ordering of the coefficients. However, after the release of the
algorithm, steganalysts were able to find a serious error in the technique that left
statistical artifacts in the stegogrammes. As a result, Neils Provos [5] created a revised
version of the OutGuess 0.1 algorithm, called OutGuess 0.2. It was ensure that the
statistical properties of the cover image were maintained after embedding, such that
stegogrammes looks statistically similar to a clean image. This would make it harder for
steganalysts to calculate the likelihood that their suspect image is a stegogramme.
The algorithm is exactly the same for OutGuess 0.2 as it was for OutGuess 0.1. The
difference lies in what happens after the information has been embedded. In OutGuess 0.2, corrections are made to the coefficients such that they appear similar to that of a
clean image in terms of frequencies of the values. This is known as statistics aware
steganography.
2.2.5 F3 As an alternative to the OutGuess 0.2 algorithm, AndreasWestfeld designed an algorithm
called F3 [6]. It was considered even more secure. The reason for this is that it did not
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instantiate the same embedding process as the JSteg and OutGuess algorithms. Instead of
avoiding embedding in DCT coefficients equal to 1, the F3 algorithm permitted
embedding in these regions, at the same time as it would still avoid embedding in zeros
and the DCT coefficients. The algorithm still embedded the message data sequentially
.Another change with this algorithm was that it did not embed directly in the least
significant bits of the DCT coefficients, but instead took the absolute value of the
coefficients first, before comparing them to the message bits. If both the absolute value of
the coefficient, and the message bit were the same, then no changes are made. If they are
different, then the absolute value of the DCT coefficient is reduced by 1. An implication
of this however, is that zero values are often created which the decoding algorithm will not be programmed to extract data from. The F5 algorithm worked around this by re-
embedding mi when the result is that a zero DCT coefficient is created.
In encoding process, F3 Technique the key part is that absolute value of the current
coefficient is taken such that it can then be compared with the message bit. If they are
both the same then we can move on to the next coefficient and attempt to embed the next
value. However, decoding process for F3 is slightly less complicated that the encoding
process because it does not concern the shrinkage issue. If the coefficient value is equal
to zero, it does not attempt to retrieve.
2.2.6 F4
The main drawback with F3 was the reality that it effectively embedded more zeros than
ones as a result of the shrinkage mechanism. This meant that when the statistical
properties of the stegogrammes are examined through its some object of embedding
became visible. This is much the same as what happened in the JSteg implementation
except a slightly different pattern is derived. In addition to this, steganalysts also found
that more odd coefficients existed in F3 stegogrammes than even coefficients. This now
meant that there were two weaknesses that could be examined when viewing the
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histogram of a suspect image. F4 was developed to remove these properties such that the
histogram would appear similar to that of a clean image.
In F4 encoding, The F4 algorithm eliminates the two weaknesses of F3 in one stroke by
mapping negative coefficients to the steganographic value, where even-negative
coefficients = 1, odd-negative coefficients = 0, even-positive coefficients = 0. Put more
simply, this means that now, if to embed a 0 in a DCT coefficient equal to -3, the result
will remain -3, where as it would have been modified to -2 using F3. This means that the
bit-flips now occur with roughly the same probability, so the histogram for the
stegogramme will not appear unstructured in terms of its frequency distribution. The
technique is to simplified approach to the encoding process of the F4 algorithm. While, in
decoding process, stegogramme is converted to obtain the quantized DCT coefficients. It
ensures that to skip the DC values and any value equal to zero. However, all other values
are used to retrieve the message data in accordance with the encoding algorithm.
2.2.7 F5
The F5 algorithm [7] is predominantly the same as the F4 algorithm, at least in terms of
its strategy for encoding the message data. However, the F5 algorithm was designed in an
attempt to improve on the F4 algorithm by minimizing the disturbance caused when
embedding the message data. This was achieved by introducing matrix encoding, and the
algorithm was the first known stego-system to make use of this technique. Hamming
coding is then used to embed potentially more than one bit per value by making no more
than 1 change to the coefficients. This is denoted to the number of bits that are to be
embedded. The F5 algorithm is therefore much more secure than the F3 and F4
algorithms.
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2.3 RELATED WORK
[8] B. P Fitzmann (1996), analyze that today most of communication occurs
electronically .There have been advancements utilizing digital multimedia signals as
vehicles for steganographic communication. These signals, which are typically audio,
video or still imagery, cover signals. Schemes where the original cover signal is needed
to reveal the hidden information are known as cover escrow.
[9] K. Tanaka et.al (1990), suggested in his method that dot patterns of the ordered dither
pixels are controlled by the information bits to be concealed. This system accommodates
2 KB of hidden information for a bi-level 256 x 256 image, yielding a payload of data or
information hiding ratio of one information bit to four cover image bits. An information
hiding ratio of 1:6 is obtained for tri-level image of the same size, the method has high
payload but is restricted to dithered images and is not resistant to errors in stego image.
[10] Bas et.al (1998) ,[11] Li,C.,Wang (2000), [12] Liao et.al,(2006) , suggested a great
variety of steganographic methods on fractal compression principles, but greatest
robustness is ensured by means of the methods suggested by their approach are directly
manipulate the code of compressed image. Building in secrecy increase the given
approaches (by means of efficient stegodetector development) will provide high level of
protection. Diversity of interchangeable image fragments allows coding of hidden data.
For this purpose in every case of comparison with the rank block all domain candidates
should be described: one should determine the set of candidates and compare with each
element of the set corresponding numerical index. In this case it is sufficient to note, that
the losses of secret information are due to the errors of non-correspondence of indices
while building-in and restoration. On the other hand the increase of staganographic
efficiency can also be achieved by increase of the number of indices.
[13] B. Wohlberg and G. de Jager (1999), in his review of fractal image coding literature
analyze that fractal coding is suitable for watermarking and steganography. The
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fundamental principle of fractal coding consists of representing an image by a contractive
transform of which the fixed point is close to that image block.
[14] Davern and Scott in (1997), proposed the use fractal image compression technique to
identify the domain blocks and range block of image fractal image compression
technique is used to find the self similar structure in the image and they choose a block
from one of the two domain sets depending on whether the data bit they are embedding is
a one or a zero.
[15] Po-Yueh Chen, Wei-En Wu (2009), suggested that by hiding more information in
the edge portions, image quality is improved while maintaining the same embedding
capacity. This merit results from the fact that human eyes can rarely percept trivial
differences in the edge regions. The embedding capacity is adjustable according to
various demands of individual users. In addition to the improvement on image quality,
the proposed approach provides respectable security as well.
[16] Guang-Yu Kang et.al (2011), presented a universal steganalysis scheme based on
fractal compression and Affinity propagation clustering. In their experiments, they
evaluate the feasibility of taking the code of fractal image compression as features to test
whether a new image is with hidden messages or not the drawback of their scheme is the
relatively high time complexity, because the fractal image compression is a time
consuming technique.
[17] Mohammed Abbas Fadhil (2010), in his system he hides a message into a stego-
image by using a mapping table and some other tables to map different values of pixels in
the image to the alphabetic letters of the message. One of the strong points in the system
is that the system does not make any changes/distortion in the stego-image, where most
steganography systems suffer from this point. Also the system tries, in its operations, to
mix the properties of some cryptographic systems to provide additional security to the
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hidden message. The experiments on the suggested system proved that it is an easy and
efficient Steganography system with a good security for the message. Therefore, this
system can be considered as a Steganography, Cryptography system and it can be used
effectively in these two fields.
[18] Provos and Honeyman (2001), Provos carried out an extensive analysis of JPEG
images downloaded from e-Bay. Using his steganalytic software, he identified several
thousands of “suspicious” images embedded with J-steg and JP Hide & Seek. A
dictionary attack was then applied in an attempt to recover the hidden message. Although
this experiment did not reveal presence of any secret messages, it does not prove that
criminals are not using steganography for communication.
[19] Simmons (1983), “Prisoners Problem”. In this scenario, Alice & Bob are in Jail, and
wish to hatch an escape plan; all their communications pass through the Warden Willie;
and if Willie detects any encrypted messages, he will frustrate their plan by throwing them in to Solitary confinement. So they must find some way of hiding their cipher text
in an innocuous looking cover text, as in the related field of cryptography, we assume
that the mechanism in use is known to the Warden and so the security must depend solely
on a secret key that Alice and Bob have somehow managed to share.
[20] Hunter and Stieglitz (1979), the quad tree partition employs the well-known image
processing technique based on a recursive splitting of selected image quadrants, enabling
the resulting partition to be represented by a tree structure in which each non-terminal
node in the tree (corresponding to some maximum range block size) and recursively
partitioning any block for which a match better than some preselected threshold is not
found. Compact coding of partition details is possible by taking advantage of the tree
structure of the partition.
[21] Harsen and Pearlman (2003) ,showed that LSBM work as a low pass filter on the
histogram of the image, which means that the histogram of the stego image contains
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fewer high frequency components compared with the histogram of its cover. Based on
this property, the authors introduced a detector using the center of mass (com) of the
Histogram Characteristic Function (HCF).
[22] Swanson, Zhu and Tewfik (1996), utilizes an approach of perceptual masking to
exploit characteristics of the human visual system (HVS) for data hiding.
[23] Backbone security has identified over 725 available Steganography tools with a
large number of these methods applied to digital images, such as bitmaps and compressed
images.
[24] Neil Johnson and Sushil Jajodia (1998), the most common approaches to
information hiding in images are least significant bit (LSB) insertion, Masking and
Filtering techniques, Algorithms and Transformations. Each of these can be applied to
various images, with varying degrees of success. Each of them suffers to varying degrees
from operations performed on images, such as cropping or resolution decrementing or
decrease in the colour depth.
[25] Bender, Gruhl and Morimoto (1996) ,introduced a technique called Patch work Pairs
of Pixels A and B are randomly chosen in the image with grey levels , A and B the
expected value of the difference A-B is zero, repeating these procedure n times.
[26] Emam (2007), a bitmap (bmp) images will be used to hide the data; Data will be
embedded inside the image using the pixels. Then the pixels of stego image can then be
accessed back in order to retrieve the hidden data inside the image. The first stage is to
come up with a new Steganography algorithm in order to hide the data inside the image
and the second stage is to come up with a decryption algorithm using data retrieving
method in order to retrieve the hidden data hidden within the stego image.
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[27] A. Jacquin (1995) , most fractal coding schemes are based on representation by
Partition Iterated Function System (PIFS), a solution to inverse problem which was first
published by Jacquin.A PIFS differs from IFS in the Individual mappings operating on a
subset of image, rather than the entire image.
[28] Khan (1967), steganography is not a new science. In fact there are several examples
from the time of ancient Greece.
[29, 30, 31] Pauate et al, Saupe et al, Wotilberg et al, (1997, 1994, 1999), Theoretical
background referred to in fractals to generate fractals considers an IFS consisting of a
collection of contractive affine transformations. The iteration procedure of applying a
number of transforms is terminated when convergence is met i.e. an attractor.
[32] Lee Yeaun-kwen and Ling Hwei Chen (1999), to maximize hiding capacity, more
than just the LSB’s of an image must be used. However, simply using more bit planes
would not solve the problem since this could produce visible marks or distortion to
appear on the processed image.
[33] Sutaone ,M. S., Khandare, M. V (2008), steganography system is designed for
encoding and decoding a secret file embedded in to an image file using random LSB
insertion method. In that method the secret data are spread out among the cover image in
a seemingly random manner. The key is used to generate pseudo-random numbers, which
will identify where and in what order the hidden message is laid out. The advantage of
this method is that it incorporates some cryptography in that of diffusion is applied to the
secret message.
[34] Anatoliy Vasyura et al (2008), define criteria for detection of hidden data in fractal
code, estimation of steganographic threat of every record in the fractal code is used. This
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estimation is based on peculiarities of fractal compression. The number of positively
estimated blocks is used to recognize data.
[35] Lisa M. Marvel et al (1998) , presented a novel approach. The process provides
method for concealing a digital signal within a cover image without increasing the size of
image. A level of security is provided by the necessity that both sender and receiver
posses the same public or private keys.
[36] Yue Wu et al (2012), suggested an algorithm takes random sequence generated by
chaotic map as the reference for embedded positions, and employs a wavelet transform to realize the embedding procedure. Generated implies a cover image could be unique in the
world. The chaotic logistic map guarantees that the sequence of embedding positions is completely unknown for an adversary and the embedding procedure is almost random-
like.
[37] Mei-Ching Chen et al (2009), proposed a steganographic scheme utilizing within
after fractal encoding procedures on images for data security. Fractal generation exploits
the concepts of iterated function systems consisting of a collection of contractive
transformations. Fractal images make use of partitioned iterated function systems to
determine self similarity within images along with approximating the original
uncompressed image. The transformed coefficients are stored in a fractal code table in
order to decode the image as an alternative to storing or transmitting image pixel values
directly. The proposed steganographic algorithm conceals secret information in the
contrasts and brightness coefficients in the code table, resulting in a stego fractal code
table. Using fractal transform as a means of steganography provides a new embedding
domain other than the existing steganography tools. The advantages of using fractal
compression for securing information within images are: no current fractal detection
methods exist, the hidden information is disseminated throughout the image in the spatial
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domain, the capacity of the image can be increased, and the decoding of the stego table
results in a visually undistorted image.
[38] Rosziati Ibrahim et al (2011), in his proposed algorithm they uses binary codes and
pixels inside an image. The zipped file is used before it is converted to binary codes to
maximize the storage of data inside the image. By applying the proposed algorithm, a
system called Steganography Imaging System (SIS) is developed. The system is then
tested to see the viability of the proposed algorithm. Various sizes of data are stored
inside the images and the PSNR (Peak signal-to-noise ratio) is also captured for each of
the images tested.
[39] Xuefeng Wang et al described a novel algorithm for embedding data into palette-
based images via grouping similar colours together and quantise them into one colour.
The proposed method yields visually non-intrusive images even maximum capacities
of the host images are used, a systematic way of selecting those groups which can
minimize the distortion is desirable.
[40] Lokesh Kumar (2012), in his proposed system cryptographic and steganographic
security is combined to give two tier securities to secret data. In proposed scheme secret
message is encrypted before hiding it into the cover image which gives high security to
secret data. Advanced encryption standard (AES) is used to encrypt secret message and
alteration component technique is used to hide encrypted secret message into cover
image. Since the resulting perceptual quality of the mixed images is good, it is hardly
attracted from eavesdropper by naked eye. Finally we can conclude that the proposed
technique is effective for secret data communication.
[41] Rosziati Ibrahim et al (2010), in his proposed a new steganography algorithm with
2layers of security. A system named SIS (Steganography Imaging System) has been
developed using the proposed algorithm. We tested few images with various sizes of data
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to be hidden. With the proposed algorithm, they found that the stego image does not have
a noticeable distortion on it (as seen by the naked eyes). SIS can be used by various users
who want to hide the data inside the image without revealing the data to other parties. SIS
maintains privacy, confidentiality and accuracy of the data.
[42] Wojciech Frączek et al (2010) suggested that Stream Control Transmission Protocol
(SCTP) is a new transport layer protocol that is due to replace TCP (Transmission
Control Protocol) and UDP (User Datagram Protocol) protocols in future IP networks.
Currently, it is implemented in such operating systems like BSD, Linux, HPUX or Sun Solaris. It is also supported in Cisco network devices operating system (Cisco IOS) and
may be used in Windows. This paper describes potential steganographic methods that
may be applied to SCTP and may pose a threat to network security. Proposed methods
utilize new, characteristic SCTP features like multi-homing and multistreaming.
Identified new threats and suggested countermeasures may be used as a supplement to
RFC 5062, which describes security attacks in SCTP protocol and can induce further
standard modifications.
[43] Hemalatha S (2013), Observes that two secret images can be hidden in one color
image and they can be regenerated without actually storing the image. This approach
results in high quality of the stegoimage having high PSNR values compared to other
methods. However the disadvantage of the approach is that it is susceptible to noise if
spatial domain techniques are used to hide the key. This can be improved if transform
domain techniques are used to hide the key. The approach is very simple and the security
level can be increased by using standard encryption techniques to encrypt the keys.
[44] Sharon Rose Govada et al (2012), he suggested a method is a combination of Word
shifting, Text Steganography and Synonym Text Steganography. So we called this as
“Three Phase Shielding Text Steganography” This method overcomes various limitations
faced by the existing Steganographic algorithms.
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[45] Gowtham dhanarasi (2012) suggested a novel method for image steganography
using block complexity analysis in wavelet domain. Many researchers have been reported
different techniques but all the methods suffer with image quality problem. So in order to
achieve good quality,
[46] Gandharba Swain et al (2013) proposed two sided, three sided and four sided side
match steganography methods are proposed. In two sides match method the two neighboring pixels such as upper and upper-right corner are exploited to take embedding
decision. In three sided side match method the three neighboring pixels such as upper,
upper-right corner and upper-left corner are exploited to take embedding decision. In four
sided side match method the four neighboring pixels such as upper, left, upper-right
corner and upper-left corner are exploited to take embedding decision. In each of these
methods the fall off boundary problem (FOBP) and fall in error problem (FIEP) are
addressed. The FOBP and FIEP conditions are checked while embedding at the sender
and while extracting at the receiver. These methods possess large hiding capacity, better
imperceptibility and high security.
2.4 COMPARISON OF VARIOUS STEGANOGRAPHIC
TECHNIQUES
Hide & Seek and JSteg algorithm follows the sequential least significant bit for
embedding. While, OutGuess 0.1 & OutGuess 0.2 are better as compared to Hide & Seek
and JSteg because it scatters the locations for embedding by using a PRNG to shuffle the
ordering. Moreover, F3 is advanced compared to all other techniques as it is embedded in
DCT coefficients equal to 1 and avoids embedding in 0.The algorithm still embeds the
message data sequentially. Further, F4 is enhanced but it results in shrinkage mechanism.
But, in F5 disturbance is minimized by using the matrix encoding scheme. Whereas
Davern Scott, Barnsley use fractal image compression technique in which self similarity
of structure in image is found through fractal compression for data hiding. While Davern
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and Scott divide the domain blocks of the image into two sets. They then use standard
fractal image compression techniques to select a domain block that matches the range
block, however they choose a block from one of the two domain sets depending on
whether the data bit they are embedding is a one or a zero.
2.5 OBJECTIVE
The objective of this work is to gain a greater understanding of information hiding
techniques like Steganography. Every steganography system that has been published at
any point of time gets replaced by a modified system later on. Consequently, new
techniques evolve after the removal of defects from their older version.
The importance of the research work is to develop a technique to hide the data inside the
digital image which can be difficult to detect and only the legitimate receiver can detect
it. Because advanced security is not maintained by the password protection but it is
gained by hiding the existence of the data which can only be done by steganography.
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2.6 SUMMARY
In this chapter, we have referred various steganographic techniques like Hide and Seek,
JSteg, OutGuess 0.1, Out Guess 0.2, F3, F4 and F5 and other work in the field of
steganography related to our work is presented. During the literature review we presented
comparison of various steganography techniques. We found that majority steganography
technique is developed using sequential embedding processes or using fractal
compression technique.
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