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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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Page 1: Shodhganga - 2. LITERATURE REVIEW 2.1 INTRODUCTION 2.2 …shodhganga.inflibnet.ac.in/bitstream/10603/46087/7/07... · 2018-07-03 · In this chapter, we review different steganographic

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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