The project report on 'Image Steganography' details the development and implementation of techniques for concealing information within digital images, primarily using least significant bit (LSB) insertion. It contrasts steganography with cryptography, emphasizing that while the latter obscures the message, the former hides its very existence. The report encompasses various aspects of steganography, including its history, types, methodologies, and applications, along with a practical implementation and analysis of image-based techniques.

1. PROJECT REPORT
ON
“IMAGE STEGANOGRAPHY”
SUBMITTED FOR THE PARTIAL FULFILLMENT OF THE DEGREE OF
BACHELOR IN ENGINEERING
IN
COMPUTER SCIENCE ENGINEERING
UNIVERSITY INSTITUTE OF ENGINEERING AND TECHNOLOGY
PANJAB UNIVERSITY, CHANDIGARH
UNIVERSITY OF ENGINEERING AND TECHNOLOGY, PANJAB UNIVERSITY, CHANDIGARH

2. CERTIFICATE
This is to certify that the following students of CSE (7th sem) have completed the work on
project entitled “IMAGE STEGANOGRAPHY”
In partial fulfillment of the requirement for the degree of BACHELOR OF
ENGINEERING to University Institute Of Engineering And Technology, Panjab
University. This is record of work carried under the guidance and supervision of the
undersigned.
Project Guide
University Institute of Engineering and Technology, Panjab University (Chandigarh)
The External Viva-Voice has been taken and the project report has been found to be
satisfactory.
External Examiner

3. ACKNOWLEDGEMENT
We would like to express our deep-felt gratitude to our Project Guide, xxx for giving us an
opportunity to work and for her advice, encouragement, and constant support. We wish to
thank her for extending us the greatest freedom in deciding the direction and scope of our
project. It has been both a privilege and a rewarding experience working with her.
We would also like to thank our colleagues here at UIET for all the wonderful times we
have had with them. Their valuable comments and suggestions have been vital to the
completion of this work. We want to thank the faculty of UIET and the staff for providing
us the means to complete our degree.
And finally, we are grateful to our parents and siblings for their love, understanding,
encouragement and support.
TABLE OF CONTENTS

4. ABSTRACT…………………………………………………………….........
1) INTRODUCTION
1.1) INTRODUCTION…………………………………………………………………………………...........
1.2) STEGANOGRAPHY vs. CRYPTOGRAPHY………………………………………………………......
1.3) HISTORY OF STEGANOGRAPHY…………………………………………………………………....
1.4) STEGANOGRAPHY TYPES………………………………………………………………………….....
1.5) IMAGE AND TRANSFORM DOMAIN………………………………………………………………...
1.5.1) Image Domain………………………………………………………………………………………….
1.5.2) Transform Domain……………………………………………………………………………………...
1.5.3) Image or Transform domain………………………………………………………………………….....
1.6) ALGORITHM REQUIREMENTS…………………………………………………………………….....
1.6.1) EVALUATION OF DIFFERENT TECHNIQUES ACCORDING TO TABLE-2………………….....
1.7) IMAGE STEGANOGRAPHY TECHNIQUES…………………………………………………………..
1.7.1) Least significant bit insertion……………………………………………………………………….......
1.7.2) Masking and filtering……………………………………………………………………………….......
1.7.3) Redundant Pattern Encoding…………………………………………………………………………....
1.7.4) Encrypt and Scatter……………………………………………………………………………………..
1.7.5) Algorithms and transformations………………………………………………………………………...
1.8) DETECTION TECHNIQUE FOR IMAGE STEGANOGRAPHY……………………………………...
1.9) IMPLEMENTATION………………………………………………………………………………….....
1.10) STEGANOGRAPHY APPLICATIONS…..…………………………………………………………....
2) PROJECTOVERVIEW
2.1) PROJECT DESCRIPTION…..…………………………………………………………………………...
2.2) REQUIREMENTS SPECIFICATION........................................................................................................
2.2.1) Hardware Requirements..............................................................................................
2.2.2) Software Requirements................................................................................................

5. 2.2.2.1) C#.NET/C++..…………………………………………………………………………………....
2.2.2.2) Microsoft Visual Studio 2010…..…………………………………………………………….
2.2.2.3) Adobe Photoshop CS5…..……………………………………………………………………...
2.3) FEASIBILITY STUDY..............................................................................................................................
2.4) OBJECTIVE…..……………………………………………………………………………………… .....
2.5) SCOPE…..…………………………………………………………………………………………….......
2.6) IMAGE DEFINITION…..……………………………………………………………………………..... .
2.7) IMAGE COMPRESSION…..………………………………………………………………………….....
2.8) LEAST SIGNIFICANT BIT…..………………………………………………………………………….
2.9) DETECTION/ ATTACKS…..…………………………………………………………………………....
2.10) BENEFITS/ DRAWBACKS…..…………………………………………………………………….......
3) PROJECTDESIGN
3.1) WBS Diagram…..………………………………………………………………………………….....
3.2) User Interface design…..………………………………………………………………………........
3.3) Encryption And Decryption Flowchart………………………………………………..
4) PROJECT IMPLEMENTATION
4.1) Coding….....………………………………………………………………………………………........
4.2) Snapshots…......………………………………………………………………………………………..
4.3) Testing…..……....…………………………………………………………………………….............
5) PROJECTAND FUTURE SCOPE
5.1) Project Scope…...……………………………………………………………………...
5.2) Future Scope……...…………………………………………………………………....
6) CONCLUSION
BIBLIOGRAPHY…………………………………………………………...
APPENDIX(PROGRAM CODE)……………………………………….....

6. ABSTRACT
In today’s world the art of sending & displaying the hidden information especially in public
places, has received more attention and faced many challenges. Therefore, different
methods have been proposed so far for hiding information in different cover media. It is
well known that encryption provides secure channels for communicating entities.
However, due to lack of covertness on these channels, an eavesdropper can identify
encrypted streams through statistical tests and capture them for further cryptanalysis.
Information hiding is an emerging research area, which encompasses applications such as
copyright protection for digital media, watermarking, fingerprinting, and steganography.
In watermarking applications, the message contains information such as owner
identification and a digital time stamp, which usually applied for copyright protection.
Fingerprint, the owner of the data set embeds a serial number that uniquely identifies the
user of the data set. This adds to copyright information to makes it possible to trace any
unauthorized used of the data set back to the user.
Steganography hide the secrete message within the host data set and presence
imperceptible and is to be reliably communicated to a receiver. The host data set is
purposely corrupted, but in a covert way, designed to be invisible to an information
analysis.

7. CHAPTER 1
INTRODUCTION
1.1) INTRODUCTION

8. One of the reasons that intruders can be successful is that most of the information they
acquire from a system is in a form that they can read and comprehend. Intruders may reveal
the information to others, modify it to misrepresent an individual or organization, or use it
to launch an attack. One solution to this problem is, through the use of steganography.
Steganography is a technique of hiding information in digital media. In contrast to
cryptography, it is not to keep others from knowing the hidden information but it is to keep
others from thinking that the information even exists.
Steganography is the art of hiding the fact that communication is taking place, by hiding
information in other information. Many different carrier file formats can be used, but digital
images are the most popular because of their frequency on the Internet. For hiding secret
information in images, there exists a large variety of steganographic techniques some are
more complex than others and all of them have respective strong and weak points. Different
applications have different requirements of the steganography technique used. For
example, some applications may require absolute invisibility of the secret information,
while others require a larger secret message to be hidden. This project intends to give an
overview of image steganography, its uses and techniques. It also attempts to identify the
requirements of a good steganographic algorithm and briefly reflects on which
steganographic techniques are more suitable for which applications.
Although, Steganography is not to be confused with Encryption, which is the process of
making a message unintelligible—Steganography attempts to hide the existence of
communication. The basic structure of Steganography is made up of three components: the
“carrier”, the message, and the key. Carrier is also known as cover-object, in which the
message is embedded and serves to hide the presence of the message. The carrier can be a
painting, a digital image, an mp3, even a TCP/IP packet among other things. It is the object
that will ‘carry’ the hidden message. A key is used to decode/decipher/discover the hidden
message. This can be anything from a password, a pattern, a black-light, or even lemon
juice. In this project we will focus on the use of Steganography within digital images
(BMP) using LSB Substitution, although the properties of Image Steganography may be
substituted with audio mp3’s, zip archives, and any other digital document format
relatively easily.

9. Basically, the model for steganography is shown in Figure 1. Message is the data that the
sender wishes to remain it confidential. It can be plain text, ciphertext, other image, or
anything that can be embedded in a bit stream such as a copyright mark, a covert
communication, or a serial number. Password is known as stego-key, which ensures that
only recipient who knows the corresponding decoding key will be able to extract the
message from a cover-object.The cover-object with the secretly embedded message is then
called the stego-object.
Recovering message from a stego-object requires the cover-object itself and a
corresponding decoding key if a stego-key was used during the encoding process. The
original image may or may not be required in most applications to extract the message.

10. There are several suitable carriers below to be the cover-object::
Network Protocols such as TCP, IP and UDP
Audio that using digital audio formats such as wav, midi, avi, mpeg, mpi and voc
File and Disk that can hides and append files by using the slack space
Text such as null characters, just alike morse code including html and java
Images file such as bmp, gif and jpg, where they can be both color and gray-scale.
In general, the information hiding process extracts redundant bits from cover-object. The
process consists of two steps:
Identification of redundant bits in a cover-object. Redundant bits are those bits that can be
modified without corrupting the quality or destroying the integrity of the cover object.
The embedding process then selects the subset of the redundant bits to be replaced with
data from a secret message. The stego-object is created by replacing the selected redundant
bits with message bits.
1.2) STEGANOGRAPHY vs. CRYPTOGRAPHY

11. Basically, the purpose of cryptography and steganography is to provide secret
communication. However, steganography is not the same as cryptography. Cryptography
hides the contents of a secret message from a malicious people, whereas steganography
even conceals the existence of the message. Steganography must not be confused with
cryptography, where we transform the message so as to make its meaning obscure to
malicious people who intercept it. Therefore, the definition of breaking the system is
different. In cryptography, the system is broken when the attacker can read the secret
message. Breaking a steganographic system need the attacker to detect that steganography
has been used and he is able to read the embedded message.
In cryptography, the structure of a message is scrambled to make it meaningless and
unintelligible unless the decryption key is available. It makes no attempt to disguise or hide
the encoded message. Basically, cryptography offers the ability of transmitting information
between persons in a way that prevents a third party from reading it. Cryptography can also
provide authentication for verifying the identity of someone or something.
In contras, steganography does not alter the structure of the secret message, but hides it
inside a cover-image so it cannot be seen. A message in cipher text, for instance, might
arouse suspicion on the part of the recipient while an “invisible” message created with
steganographic methods will not. In other word, steganography prevents an unintended
recipient from suspecting that the data exists. In addition, the security of classical
steganography system relies on secrecy of the data encoding system. Once the encoding
system is known, the steganography system is defeated.
It is possible to combine the techniques by encrypting message using cryptography and
then hiding the encrypted message using steganography. The resulting stego-image can be
transmitted without revealing that secret information is being exchanged. Furthermore,
even if an attacker were to defeat the steganographic technique and detect the message
from the stego-object, he would still require the cryptographic decoding key to decipher
the encrypted message. Table 1 shows that both technologies have counter advantages and
disadvantages.

12. HISTORY OF STEGANOGRAPHY

13. The word “Steganography” technically means “covered or hidden writing”. Its ancient
origins can be traced back to 440 BC. Although the term steganography was only coined
at the end of the 15th century, the use of steganography dates back several millennia.
Some examples of use of Steganography is past times are:
In ancient Greece, messages were hidden on the back of wax writing tables where someone
would peel off the wax that was or written on the stomachs of rabbits.
In Ancient Greece they used to select messengers and shave their head, they would then
write a message on their head. Once the message had been written the hair was allowed to
grow back. After the hair grew back the messenger was sent to deliver the message, the
recipient would shave off the messengers hair to see the secrete message.
During World War 2 invisible ink was used to write information on pieces of paper so that
the paper appeared to the average person as just being blank pieces of paper. Liquids such
as milk, vinegar and fruit juices were used, because when each one of these substances
were heated they darken and become visible to the human eye. Invisible ink has been in
use for centuries—for fun by children and students and for serious espionage by spies and
terrorists.
Cryptography became very common place in the middle ages. Secret writing was employed
by the Catholic Church in its various struggles down the ages and by the major
governments of the time. Steganography was normally used in conjunction with
cryptography to further hide secret information.
1.4) STEGANOGRAPHY TYPES
The majority of today’s steganographic systems uses multimedia objects like image, audio,
video etc as cover media because people often transmit digital pictures over email and other

14. Internet communication. In modern approach, depending on the nature of cover object,
steganography can be divided into five types:
• Text Steganography
• Image Steganography
• Audio Steganography
• Video Steganography
• Protocol Steganography
So, in the modern age so many steganographic techniques have been designed which work
with the above concerned objects. More often in today’s security advancement, we
sometimes come across a combination of Cryptography and Steganography to achieve data
privacy over secrecy. Various software tools are also available in this regard.
IMAGE AND TRANSFORM DOMAIN
Image steganography techniques can be divided into two groups: those in the Image
Domain and those in the Transform Domain. Image – also known as spatial – domain

15. techniques embed messages in the intensity of the pixels directly, while for transform –
also known as frequency – domain, images are first transformed and then the message is
embedded in the image. Image domain techniques encompass bit-wise methods that apply
bit insertion and noise manipulation and are sometimes characterised as “simple systems”.
The image formats that are most suitable for image domain steganography are lossless and
the techniques are typically dependent on the image format. Steganography in the
transform domain involves the manipulation of algorithms and image transforms. These
methods hide messages in more significant areas of the cover image, making it more robust.
Many transform domain methods are independent of the image format and the embedded
message may survive conversion between lossy and lossless compression.
In the next sections steganographic algorithms will be explained in categories according to
image file formats and the domain in which they are performed.
1.5.1) Image Domain
Least Significant Bit
Least significant bit (LSB) insertion is a common, simple approach to embedding
information in a cover image. The least significant bit (in other words, the 8th bit) of some
or all of the bytes inside an image is changed to a bit of the secret message. When using a
24-bit image, a bit of each of the red, green and blue color components can be used, since
they are each represented by a byte. (It has been explained in detail later).
LSB and Palette BasedImages
Palette based images, for example GIF images, are another popular image file format
commonly used on the Internet. By definition a GIF image cannot have a bit depth greater
than 8, thus the maximum number of colors that a GIF can store is 256. GIF images are
indexed images where the colors used in the image are stored in a palette, sometimes
referred to as a color lookup table. Each pixel is represented as a single byte and the pixel
data is an index to the color palette. The colors of the palette are typically ordered from the
most used color to the least used colors to reduce lookup time.
GIF images can also be used for LSB steganography, although extra care should be taken.
The problem with the palette approach used with GIF images is that should one change the
least significant bit of a pixel, it can result in a completely different color since the index

16. to the color palette is changed. If adjacent palette entries are similar, there might be little
or no noticeable change, but should the adjacent palette entries be very dissimilar, the
change would be evident. One possible solution is to sort the palette so that the color
differences between consecutive colors are minimized. Another solution is to add new
colors which are visually similar to the existing colors in the palette. This requires the
original image to have less unique colors than the maximum number of colors (this value
depends on the bit depth used). Using this approach, one should thus carefully choose the
right cover image. Unfortunately any tampering with the palette of an indexed image leaves
a very clear signature, making it easier to detect. A final solution to the problem is to use
greyscale images. In an 8-bit greyscale GIF image, there are 256 different shades of grey.
The changes between the colors are very gradual, making it harder to detect.
1.5.2) Transform Domain
To understand the steganography algorithms that can be used when embedding data in the
transform domain, one must first explain the type of file format connected with this
domain. The JPEG file format is the most popular image file format on the Internet, because
of the small size of the images.
JPEG compression
To compress an image into JPEG format, the RGB color representation is first converted
to a YUV representation. In this representation the Y component corresponds to the
luminance (or brightness) and the U and V components stand for chrominance (or color).
According to research the human eye is more sensitive to changes in the brightness
(luminance) of a pixel than to changes in its color. This fact is exploited by the JPEG
compression by down-sampling the color data to reduce the size of the file. The color
components (U and V) are halved in horizontal and vertical directions, thus decreasing the
file size by a factor of 2. The next step is the actual transformation of the image. For JPEG,

17. the Discrete Cosine Transform (DCT) is used, but similar transforms are for example the
Discrete Fourier Transform (DFT). These mathematical transforms convert the pixels in
such a way as to give the effect of “spreading” the location of the pixel values over part of
the image. The DCT transforms a signal from an image representation into a frequency
representation, by grouping the pixels into 8 × 8 pixel blocks and transforming the pixel
blocks into 64 DCT coefficients each. A modification of a single DCT coefficient will
affect all 64 image pixels in that block. The next step is the quantization phase of the
compression. Here another biological property of the human eye is exploited: The human
eye is fairly good at spotting small differences in brightness over a relatively large area,
but not so good as to distinguish between different strengths in high frequency brightness.
This means that the strength of higher frequencies can be diminished, without changing the
appearance of the image. JPEG does this by dividing all the values in a block by a
quantization coefficient. The results are rounded to integer values and the coefficients are
encoded using Huffman coding to further reduce the size.
JPEG steganography
Originally it was thought that steganography would not be possible to use with JPEG
images, since they use lossy compression which results in parts of the image data being
altered. One of the major characteristics of steganography is the fact that information is
hidden in the redundant bits of an object and since redundant bits are left out when using
JPEG it was feared that the hidden message would be destroyed. Even if one could
somehow keep the message intact it would be difficult to embed the message without the
changes being noticeable because of the harsh compression applied. However, properties
of the compression algorithm have been exploited in order to develop a steganographic
algorithm for JPEGs.
One of these properties of JPEG is exploited to make the changes to the image invisible to
the human eye.
During the DCT transformation phase of the compression algorithm, rounding errors occur
in the coefficient data that are not noticeable. Although this property is what classifies the
algorithm as being lossy, this property can also be used to hide messages. It is neither
feasible nor possible to embed information in an image that uses lossy compression, since
the compression would destroy all information in the process. Thus it is important to

18. recognize that the JPEG compression algorithm is actually divided into lossy and lossless
stages. The DCT and the quantization phase form part of the lossy stage, while the Huffman
encoding used to further compress the data is lossless. Steganography can take place
between these two stages. Using the same principles of LSB insertion the message can be
embedded into the least significant bits of the coefficients before applying the Huffman
encoding. By embedding the information at this stage, in the transform domain, it is
extremely difficult to detect, since it is not in the visual domain.
1.5.3) Image or Transform domain
As seen in the diagram above, some steganographic algorithms can either be categorized
as being in the image domain or in the transform domain depending on the implementation.
Patchwork
Patchwork is a statistical technique that uses redundant pattern encoding to embed a
message in an image. The algorithm adds redundancy to the hidden information and then
scatters it throughout the image. A Pseudo random generator is used to select two areas of
the image (or patches), patch A and patch B. All the pixels in patch A is lightened while
the pixels in patch B are darkened. In other words the intensities of the pixels in the one
patch are increased by a constant value, while the pixels of the other patch are decreased
with the same constant value. The contrast changes in this patch subset encodes one bit and
the changes are typically small and imperceptible, while not changing the average
luminosity.
A disadvantage of the patchwork approach is that only one bit is embedded. One can embed
more bits by first dividing the image into sub-images and applying the embedding to each
of them. The advantage of using this technique is that the secret message is distributed over
the entire image, so should one patch be destroyed, the others may still survive. This
however, depends on the message size, since the message can only be repeated throughout
the image if it is small enough. If the message is too big, it can only be embedded once.
The patchwork approach is used independent of the host image and proves to be quite
robust as the hidden message can survive conversion between lossy and lossless
compression.
Spread Spectrum

19. In spread spectrum techniques, hidden data is spread throughout the cover-image making
it harder to detect. A system proposed by Marvel et al. combines spread spectrum
communication, error control coding and image processing to hide information in images.
Spread spectrum communication can be defined as the process of spreading the bandwidth
of a narrowband signal across a wide band of frequencies. This can be accomplished by
adjusting the narrowband waveform with a wideband waveform, such as white noise. After
spreading, the energy of the narrowband signal in any one frequency band is low and
therefore difficult to detect. In spread spectrum image steganography the message is
embedded in noise and then combined with the cover image to produce the stego-image.
Since the power of the embedded signal is much lower than the power of the cover image,
the embedded image is not perceptible to the human eye or by computer analysis without
access to the original image.
1.6) ALGORITHM REQUIREMENTS
All steganographic algorithms have to comply with a few basic requirements. The most
important requirement is that a steganographic algorithm has to be imperceptible. These
requirements are as follows:

20. Invisibility – The invisibility of a steganographic algorithm is the first and foremost
requirement, since the strength of steganography lies in its ability to be unnoticed by the
human eye. The moment that one can see that an image has been tampered with, the
algorithm is compromised.
Payload capacity – Unlike watermarking, which needs to embed only a small amount of
copyright information, steganography in other hand requires sufficient embedding
capacity.
Robustness against statistical attacks – Statistical steganalysis is the practice of detecting
hidden information through applying statistical tests on image data. Many steganographic
algorithms leave a “signature” when embedding information that can be easily detected
through statistical analysis. To be able to pass by a warden without being detected, a
steganographic algorithm must not leave such a mark in the image as be statistically
significant.
Robustness against image manipulation – In the communication of a stego-image by
trusted systems, the image may undergo changes by an active warden in an attempt to
remove hidden information. Image manipulation, such as cropping or rotating, can be
performed on the image before it reaches its destination. Depending on the manner in which
the message is embedded, these manipulations may destroy the hidden message. It is
preferable for steganographic algorithms to be robust against either malicious or
unintentional changes to the image.
Independent of file format – With many different image file formats used on the Internet,
it might seem suspicious that only one type of file format is continuously communicated
between two parties. The most powerful steganographic algorithms thus possess the ability
to embed information in any type of file. This also solves the problem of not always being
able to find a suitable image at the right moment, in the right format to use as a cover image.
Unsuspicious files – This requirement includes all characteristics of a steganographic
algorithm that may result in images that are not used normally and may cause suspicion.
Abnormal file size, for example, is one property of an image that can result in further
investigation of the image by a warden.

21. The levels at which the algorithms satisfy the requirements are defined as high, medium
and low. A high level means that the algorithm completely satisfies the requirement, while
a low level indicates that the algorithm has a weakness in this requirement. A medium level
indicates that the requirement depends on outside influences, for example the cover image
used. LSB in GIF images has the potential of hiding a large message, but only when the
most suitable cover image has been chosen.
The ideal, in other words a perfect steganographic algorithm would have a high level in
every requirement. Unfortunately its hard to develop an algorithm that satisfies all of the
requirements. Thus a trade-off will exist in most cases, depending on which requirements
are more important for the specific application.
1.6.1) EVALUATION OF DIFFERENT TECHNIQUES ACCORDING TO TABLE-
2
LSB in BMP– When embedding a message in a “raw” image, which has not been changed
with compression, such as a BMP, there exists a trade-off between the invisibility of the
message and the amount of information that can be embedded. A BMP is capable of hiding

22. quite a large message, but the fact that more bits are altered results in a larger possibility
that the altered bits can be seen with the human eye. The main disadvantage regarding LSB
in BMP images is surely the suspicion that might arise from a very large BMP image being
transmitted between parties, since BMP is not widely used anymore.
Suggested applications: LSB in BMP is most suitable for applications where the focus is
on the amount of information to be transmitted and not on the secrecy of that information.
LSB in GIF – The strong and weak points regarding embedding information in GIF images
using LSB are more or less the same as those of using LSB with BMP. The main difference
is that since GIF images only have a bit depth of 8, the amount of information that can be
hidden is less than with BMP. GIF images are especially vulnerable to statistical – or visual
attacks – since the palette processing that has to be done leaves a very definite signature on
the image. This approach is dependent on the file format as well as the image itself, since
a wrong choice of image can result in the message being visible.
Suggestedapplications: LSB in GIF is a very efficient algorithm to use when embedding
a reasonable amount of data in a greyscale image.
JPEG compression – The process of embedding information during JPEG compression
results in a stego image with a high level of invisibility, since the embedding takes place
in the transform domain. JPEG is the most popular image file format on the Internet and
the image sizes are small because of the compression, thus making it the least suspicious
algorithm to use. However, the process of the compression is a very mathematical process,
making it more difficult to implement.
Suggested applications: The JPEG file format can be used for most applications of
steganography, but is especially suitable for images that have to be communicated over an
open systems environment like the Internet.
Patchwork – The biggest disadvantage of the patchwork approach is the small amount of
information that can be hidden in one image. This property can be changed to accommodate
more information but one may have to sacrifice the secrecy of the information. Patchwork’s
main advantage, however, is its robustness against malicious or unintentional image
manipulation. Should a stego image using patchwork be cropped or rotated, some of the
message data may be lost but since the message is repeatedly embedded in the image, most
of the information will survive.

23. Suggested applications: Patchwork is most suitable for transmitting a small amount of
very sensitive information.
Spread spectrum – Spread spectrum techniques satisfies most requirements and is
especially robust against statistical attacks, since the hidden information is scattered
throughout the image, while not changing the statistical properties.
Suggestedapplications: Spread spectrum techniques can be used for most steganography
applications, although its highly mathematical and intricate approach may prove too much
for some.
1.7) IMAGE STEGANOGRAPHY TECHNIQUES
To hide information, straight message insertion may encode every bit of information in the
image or selectively embed the message in “noisy” areas that draw less attention- those
areas where there is a great deal of natural color variation. The message may also be
scattered randomly throughout the image. A number of ways exist to hide information in
digital media. Common techniques which with varying degrees of success include:
Least significant bit insertion
Masking and filtering
Redundant Pattern Encoding
Encrypt and Scatter
Algorithms and transformations

24. 1.7.1) Least significant bit insertion
Least significant bits (LSB) insertion is a simple approach to embedding information in image
file. The simplest steganographic techniques embed the bits of the message directly into least
significant bit plane of the cover-image in a deterministic sequence. Modulating the least
significant bit does not result in human-perceptible difference because the amplitude of the
change is small. In this method the LSB of a byte is replaced with an M’s bit. This technique
works well for image, audio and video steganography. To the human eye, the resulting
image will look identical to the cover object.
1.7.2) Masking and filtering
Masking and filtering techniques, usually restricted to 24 bits and gray scale images, hide
information by marking an image, in a manner similar to paper watermarks. The techniques
performs analysis of the image, thus embed the information in significant areas so that the
hidden message is more integral to the cover image than just hiding it in the noise level. They
hide info in a way similar to watermarks on actual paper and are sometimes used as digital
watermarks. Masking images entails changing the luminance of the masked area. The
smaller the luminance change, the less of a chance that it can be detected.
Masking is more robust than LSB insertion with respect to compression, cropping, and
some image processing. Masking techniques embed information in significant areas so that
the hidden message is more integral to the cover image than just hiding it in the “noise”
level. This makes it more suitable than LSB with, for instance, lossy JPEG images.
1.7.3) Redundant Pattern Encoding
Patchwork and other similar tools do redundant pattern encoding, which is a sort of spread
spectrum technique. It works by scattering the message throughout the picture. This makes
the image more resistant to cropping and rotation. Smaller secret images work better to
increase the redundancy embedded in the cover image, and thus make it easier to recover
if the stego-image is manipulated.
1.7.4) Encrypt and Scatter
The Encrypt and Scatter technique tries to emulate white noise. It is mostly used in image
steganography. White Noise Storm is one such program that employs spread spectrum and
frequency hopping. It does this by scattering the message throughout an image on eight

25. channels within a random number that is generated by the previous window size and data
channel. The channels then swap rotate, and interlace amongst each other. Each channel
represents one bit and as a result there are many unaffected bits in each channel. This
technique is a lot harder to extract a message out of than an LSB scheme because to decode
you must first detect that a hidden image exists and extract the bit pattern from the file.
While that is true for any stego-image you will also need the algorithm and stego key to
decode the bit pattern, both of which are not required to recover a message from LSB.
Some people prefer this method due to the considerable amount of extra effort that
someone without the algorithm and stego-key would have to go through to extract the
message. Even though White Noise Storm provides extra security against message
extraction it is just as susceptible as straight LSB to image degradation due to image
processing.
1.7.5) Algorithms and transformations
Transform techniques embed the message by modulating coefficients in a transform domain,
such as the Discrete Cosine Transform (DCT) used in JPEG compression, Discrete Fourier
Transform, or Wavelet Transform. These methods hide messages in significant areas of the
cover-image, which make them more robust to attack. Transformations can be applied over the
entire image, to block through out the image, or other variants. LSB modification technique
for images does hold good if any kind of compression is done on the resultant stego-image
e.g. JPEG, GIF etc.
JPEG images use the discrete cosine transform to achieve compression. DCT is a lossy
compression transform because the cosine values cannot be calculated exactly, and
repeated calculations using limited precision numbers introduce rounding errors into the
final result. Variances between original data values and restored data values depend on the
method used to calculate DCT.

26. 1.8) DETECTION TECHNIQUE FOR IMAGE STEGANOGRAPHY
Even though stego-images can rarely be spotted by the naked eye, they usually leave behind
some type of fingerprint or statistical hint that they have been modified. It is those
discrepancies which an analysis tool may be able to detect. Since some techniques and their
effects are commonly known, a statistical analysis of an image can be performed to check
for a hidden message(s) in it.
A widely used technique for image scanning involves statistical analysis. Most
steganographic algorithms that work on images, assume that the least significant bit is more
or less random. This is however, an incorrect assumption. While the LSB might not seem
to be of much importance, applying a filter which only shows the least significant bits, will
still produce a recognizable image. Since this is the case, it can be concluded that the LSB
are not random at all, but actually contain information about the whole image. When
inserting a hidden message into an image, this property changes. Especially with encrypted

27. data, which has very high entropy, the LSB of the cover image will no longer contain
information about the original, but because of the modifications they will now be more or
less random. With a statistical analysis on the LSB, the difference between random values
and real image values can easily be detected. Using this technique, it is also possible to
detect messages hidden inside JPEG files with the DCT method, since this also involves
LSB modifications, even though these take place in the frequency domain.
1.9) IMPLEMENTATION
There are currently three effective methods in applying Image Steganography in spatial
domain:
LSB Substitution
Blocking (DCT)
Palette Modification.
LSB (Least Significant Bit) Substitution is the process of modifying the least significant
bit of the pixels of the carrier image.
Blocking works by breaking up an image into “blocks” and using Discrete Cosine
Transforms (DCT). Each block is broken into 64 DCT coefficients that approximate
luminance and color—the values of which are modified for hiding messages.
Palette Modification replaces the unused colors within an image’s color palette with
colors that represent the hidden message.

28. 1.10) STEGANOGRAPHY APPLICATIONS
Image Steganography has many applications, especially in today’s modern, high-tech
world. Steganography can be used anytime you want to hide data. There are many reasons
to hide data but they all boil down to the desire to prevent unauthorized persons from
becoming aware of the existence of a message. In the business world steganography can
be used to hide a secret chemical formula or plans for a new invention. Steganography can
also be used for corporate espionage by sending out trade secrets without anyone at the
company being any the wiser. Steganography can also be used in the noncommercial sector
to hide information that someone wants to keep private.
Privacy and anonymity is a concern for most people on the internet. Image Steganography
allows for two parties to communicate secretly and covertly. It allows for some morally-
conscious people to safely whistle blow on internal actions; it allows for copyright
protection on digital files using the message as a digital watermark. One of the other main
uses for Image Steganography is for the transportation of high-level or top-secret
documents between international governments. While Image Steganography has many
legitimate uses, it can also be quite nefarious. It can be used by hackers to send viruses and
trojans to compromise machines, and also by terrorists and other organizations that rely on
covert operations to communicate secretly and safely.
Spies have used it since the time of the Greeks to pass messages undetected. Terrorists can
also use steganography to keep their communications secret and to coordinate attacks. It is
exactly this potential that we will investigate in the next section. Because you can hide
information without the cover source changing, steganography can also be used to
implement watermarking. Although the concept of watermarking is not necessarily
steganography, there are several steganographic techniques that are being used to store
watermarks in data. The main difference is on intent, while the purpose of steganography
is hiding information, watermarking is merely extending the cover source with extra
information. Since people will not accept noticeable changes in images, audio or video files
because of a watermark, steganographic methods can be used to hide this.
In feature tagging, captions, annotations, time stamps, and other descriptive elements can
be embedded inside an image. Copying the stego–image also copies of the embedded
features and only parties who posses the decoding stego-key will be able to extract and

29. view the features. On the other hand, secret communication does not advertise a covert
communication by using steganography. Therefore, it can avoid scrutiny of the sender,
message and recipient. This is effective only if the hidden communication is not detected
by the others people.
CHAPTER 2

30. PROJECT OVERVIEW
2.1) PROJECT DESCRIPTION
There are two trends at the time to implement steganographic algorithms: the methods that
work in the spatial domain (altering the desired characteristics on the file itself) and the
methods that work in the transform domain (performing a series of changes to the cover
image before hiding information. To select the best areas the Discrete Cosine Transform
DCT, Wavelet Transform, etc. are used).
While the algorithms that work in the transform domain are more robust, that is, more
resistant to attacks, the algorithms that work in the spatial domain are simpler and faster.
The best known steganographic method that works in the spatial domain is the LSB (Least
Significant Bit), which replaces the least significant bits of pixels selected to hide the
information. This method has several implementation versions that improve the algorithm
in certain aspects.
We have chosen to implement LSB Substitution in our project because of its ubiquity
among carrier formats and message types. With LSB Substitution, we could easily change
from Image Steganography to Audio Steganography and hide a zip archive instead of a text

31. message. LSB Substitution lends itself to become a very powerful Steganographic method
with few limitations. LSB Substitution works by iterating through the pixels of an image
and extracting the ARGB values. It then separates the color channels and gets the least
significant bit. Meanwhile, it also iterates through the characters of the message setting the
bit to its corresponding binary value.
In our project, we have made an APPLICATION on steganography to be used by the Indian
Defence Services in which we provide a user friendly interface to encrypt a data file in a
bmp image format. We have used a variation of LSB technique where we are encoding the
last 4 LSBs in direct proportion to the first 4 MSBs, by which I mean that, if in out image,
the 8 bits are: 11110101, and we need to encode 1101 in our image bits, we will encode all
the last four bits and the output will be, 11111101.
Similarly, if the first four bits of our image are 11100110, we will encode the last 3 bits
and so on…

32. The cover-image will be combined with the message. This will produce the output called
stego-image. Figure 2 is illustrating the process. The Stego-image seems identical to the
cover-image. However, there are hidden message that imperceptible. This process simply
embedded the message into the cover-image without supplied any password or stego-key.
At this stage, we decided to do so because we have to understand the ways of LSB insert
the message bit into the image and extract the message from the stego-image produced.

33. 2.2) REQUIREMENT SPECIFICATION
2.2.1) Hardware Requirements:
Minimum 1GB RAM, Minimum 10 GB Hard Disk Space
Operating System supported: Windows XP, Windows Vista, Windows 7
2.2.2) Software Requirements:
Visual Studio 2010, .NET Framework 4.0 and Adobe Photoshop CS5 installed on the
system.
SOFTWARE DESCRIPTION
2.2.2.1) C#.NET/ C++
C# is a relatively new language that was unveiled to the world when Microsoft announced the
first version of its .NETFramework in July 2000.Since then its popularity has rocketed, and it
has arguably become the language of choicefor both Windows and Web developers who use the
.NET Framework. Part of the appeal of C# comes from its clear syntax, which derives from
C/C++ but simplifies some things that have previously discouraged some programmers.
Despite this simplification, C# has retained the power of C++, and there is now no reason not
to move into C#. The language is not difficult and it’s a great one to learn elementary
programming techniques with.
.
C#.NET a window application framework developed and marketed by Microsoft to allow
programmers to build dynamic web sites, web applications and web services is used for
our project’s software coding.
By design, C# is the programming language that most directly reflects the underlying
Common Language Infrastructure (CLI). Most of its intrinsic types correspond to value-
types implemented by the CLI framework. However, the language specification does not
state the code generation requirements of the compiler: that is, it does not state that a C#
compiler must target a Common Language Runtime, or generate Common Intermediate

34. Language (CIL), or generate any other specific format. Theoretically, a C# compiler could
generate machine code like traditional compilers of C++ or Fortran.
Some notable distinguishing features of C# are:
There are no global variables or functions. All methods and members must be declared
within classes. Static members of public classes can substitute for global variables and
functions.
Local variables cannot shadow variables of the enclosing block, unlike C and C++.
Variable shadowing is often considered confusing by C++ texts.
C# supports a strict Boolean datatype, bool. Statements that take conditions, such as while
and if, require an expression of a type that implements the true operator, such as the boolean
type. While C++ also has a boolean type, it can be freely converted to and from integers,
and expressions such as if(a) require only that a is convertible to bool, allowing a to be an
int, or a pointer. C# disallows this "integer meaning true or false" approach on the grounds
that forcing programmers to use expressions that return exactly bool can prevent certain
types of common programming mistakes in C or C++ such as if (a = b) (use of assignment
= instead of equality ==).
In C#, memory address pointers can only be used within blocks specifically marked as
unsafe, and programs with unsafe code need appropriate permissions to run. Most object
access is done through safe object references, which always either point to a "live" object
or have the well-defined null value; it is impossible to obtain a reference to a "dead" object
(one which has been garbage collected), or to a random block of memory. An unsafe
pointer can point to an instance of a value-type, array, string, or a block of memory
allocated on a stack. Code that is not marked as unsafe can still store and manipulate
pointers through the System.IntPtr type, but it cannot dereference them.
Managed memory cannot be explicitly freed; instead, it is automatically garbage collected.
Garbage collection addresses the problem of memory leaks by freeing the programmer of
responsibility for releasing memory which is no longer needed.
In addition to the try...catch construct to handle exceptions, C# has a try...finally construct
to guarantee execution of the code in the finally block.

35. Multiple inheritance is not supported, although a class can implement any number of
interfaces. This was a design decision by the language's lead architect to avoid
complication and simplify architectural requirements throughout CLI.
C# is more type safe than C++. The only implicit conversions by default are those which
are considered safe, such as widening of integers. This is enforced at compile-time, during
JIT, and, in some cases, at runtime. There are no implicit conversions between booleans
and integers, nor between enumeration members and integers (except for literal 0, which
can be implicitly converted to any enumerated type). Any user-defined conversion must be
explicitly marked as explicit or implicit, unlike C++ copy constructors and conversion
operators, which are both implicit by default.
Enumeration members are placed in their own scope.
C# provides properties as syntactic sugar for a common pattern in which a pair of methods,
accessor (getter) and mutator (setter) encapsulate operations on a single attribute of a class.
Full type reflection and discovery is available.
C# currently (as of version 4.0) has 77 reserved words.
Checked exceptions are not present in C# (in contrast to Java). This has been a conscious
decision based on the issues of scalability and versionability.
2.2.2.2) Microsoft Visual Studio 2010
Microsoft Visual Studio 2010 version 10.0.30319.1 RTMRel. ©2010 Microsoft
Corporation.

36. 2.2.2.3) Adobe Photoshop CS5
It’s the latest version of Adobe Photoshop CS5 version 12.0 x64. © 1990-2010 Adobe
Systems Incorporated. Its snapshot is given below:

37. 2.3) FEASILBILITY STUDY
Technical Feasibility
Image Steganography is a defence application with a back-end coding done in C#.NET that
allows a user to hide data (documents or text files) in an image. The user friendly interface
requires no technical skills and is easy to operate on. Visual Studio 2010 using .NET 4.0
framework is used for the design and coding purposes. Adobe Photoshop CS5 has also
been used to design the header of the application with an animated sequence of images.
Economic Feasibility
The project is economic and highly beneficial project as far as the cost of development is
considered. No extra costs were incurred apart from the software used.
Operational Feasibility
The project is operationally very feasible as it is user-friendly, the user doesn’t need any
kind of knowledge about the software used in the project. The project is also really helpful
as the user can use it to send encrypted data at any moment of time using the internet or
the LAN system.
2.4) OBJECTIVE
This project comprehends the following objectives:
To produce security tool based on steganographic techniques.
To explore LSB techniques of hiding data using steganography.
2.5) SCOPE
The scope of the project as follows:
Implementation of a variation of LSB technique for hiding information i.e. text in image
files.
2.6) IMAGE DEFINITION

38. To a computer, an image is a collection of numbers that constitute different light intensities
in different areas of the image. This numeric representation forms a grid and the individual
points are referred to as pixels. Most images on the Internet consists of a rectangular map
of the image’s pixels (represented as bits) where each pixel is located and its color. These
pixels are displayed horizontally row by row. The number of bits in a color scheme, called
the bit depth, refers to the number of bits used for each pixel.
The smallest bit depth in current color schemes is 8, meaning that there are 8 bits used to
describe the color of each pixel. Monochrome and greyscale images use 8 bits for each
pixel and are able to display 256 different colors or shades of grey. Digital color images
are typically stored in 24-bit files and use the RGB color model, also known as true color.
All color variations for the pixels of a 24-bit image are derived from three primary colors:
red, green and blue, and each primary color is represented by 8 bits. Thus in one given
pixel, there can be 256 different quantities of red, green and blue, adding up to more than
16-million combinations, resulting in more than 16-million colors. Not surprisingly the
larger amount of colors that can be displayed, the larger the file size. For this project, we
are considering 8-bit images.
2.7) IMAGE COMPRESSION

39. When working with larger images of greater bit depth, the images tend to become too large
to transmit over a standard Internet connection. In order to display an image in a reasonable
amount of time, techniques must be incorporated to reduce the image’s file size. These
techniques make use of mathematical formulas to analyse and condense image data,
resulting in smaller file sizes. This process is called compression. In images there are two
types of compression: lossy and lossless. Both methods save storage space, but the
procedures that they implement differ. Lossy compression creates smaller files by
discarding excess image data from the original image. It removes details that are too small
for the human eye to differentiate, resulting in close approximations of the original image,
although not an exact duplicate. An example of an image format that uses this compression
technique is JPEG (Joint Photographic Experts Group).
Lossless compression, on the other hand, never removes any information from the original
image, but instead represents data in mathematical formulas. The original image’s integrity
is maintained and the decompressed image output is bit-by-bit identical to the original
image input. The most popular image formats that use lossless compression is GIF
(Graphical Interchange Format) and 8-bit BMP (a Microsoft
Windows bitmap file).
Compression plays a very important role in choosing which steganographic algorithm to
use. Lossy compression techniques result in smaller image file sizes, but it increases the
possibility that the embedded message may be partly lost due to the fact that excess image
data will be removed. Lossless compression though, keeps the original digital image intact
without the chance of lost, although is does not compress the image to such a small file
size. Different steganographic algorithms have been developed for both of these
compression types and will be explained in the following sections.
2.8) LEAST SIGNIFICANT BIT

40. Least significant bit (LSB) insertion is a common, simple approach to embedding
information in a cover image. The least significant bit (in other words, the 8th bit) of some
or all of the bytes inside an image is changed to a bit of the secret message. When using a
24-bit image, a bit of each of the red, green and blue color components can be used, since
they are each represented by a byte. In other words, one can store 3 bits in each pixel. An
800 × 600 pixel image, can thus store a total amount of 1,440,000 bits or 180,000 bytes of
embedded data. For example a grid for 3 pixels of a 24-bit image can be as follows:
(00101101 00011100 11011100)
(10100110 11000100 00001100)
(11010010 10101101 01100011)
When the number 200, which binary representation is 11001000, is embedded into the
least significant bits of this part of the image, the resulting grid is as follows:
(00101101 00011101 11011100)
(10100110 11000101 00001100)
(11010010 10101100 01100011)
Although the number was embedded into the first 8 bytes of the grid, only the 3 underlined
bits needed to be changed according to the embedded message. On average, only half of
the bits in an image will need to be modified to hide a secret message using the maximum
cover size. Since there are 256 possible intensities of each primary color, changing the LSB
of a pixel results in small changes in the intensity of the colors. These changes cannot be
perceived by the human eye - thus the message is successfully hidden. With a well-chosen
image, one can even hide the message in the least as well as second to least significant bit
and still not see the difference.
In the above example, consecutive bytes of the image data – from the first byte to the end
of the message – are used to embed the information. This approach is very easy to detect.
A slightly more secure system is for the sender and receiver to share a secret key that
specifies only certain pixels to be changed. Should an adversary suspect that LSB

41. steganography has been used, he has no way of knowing which pixels to target without the
secret key.
In its simplest form, LSB makes use of BMP images, since they use lossless compression.
Unfortunately to be able to hide a secret message inside a BMP file, one would require a
very large cover image. Nowadays, BMP images of 800 × 600 pixels are not often used on
the Internet and might arouse suspicion. For this reason, LSB steganography has also been
developed for use with other image file formats.
2.9) DETECTION/ ATTACKS
While the purpose of Steganography is to hide messages, it may not be very effective at
doing so. There are several attacks that one may execute to test for Steganographed images.
They are:
Visual Attacks
Enhanced LSB Attacks
Chi-Square Analysis, and
Other statistical analyses.
In performing a visual attack you must have the original “virgin” image to compare it the
Steganographed image and visually compare the two for artifacts.
In the Enhanced LSB Attack, you process the image for the least significant bits and if
the LSB is equal to one, multiply it by 255 so that it becomes its maximum value.
Chi-Square Analysis calculates the average LSB and constructs a table of frequencies and
Pair of Values; it takes the data from these two tables and performs a chi-square test. It
measures the theoretical vs. calculated population difference. The Chi-Square Analysis
calculates the chi-square for every 128 bytes of the image. As it iterates through, the chi-
square value it calculates becomes more and more accurate until too large of a dataset has
been produced. Because this attack relies on statistical analysis it cannot detect patterns or
Steganography on very complex images with lots of noise than one can detect through
visualization of the Enhanced LSB’s.
2.10) BENEFITS/ DRAWBACKS

42. The advantages of LSB are its simplicity to embed the bits of the message directly into
the LSB plane of cover-image and many techniques use these methods. Modulating the
LSB does not result in a human-perceptible difference because the amplitude of the change
is small. Therefore, to the human eye, the resulting stego-image will look identical to the
cover-image. This allows high perceptual transparency of LSB.
However, there are a few weaknesses of using LSB. It is very sensitive to any kind of
filtering or manipulation of the stego-image. Scaling, rotation, cropping, addition of noise,
or lossy compression to the stego-image will destroy the message.
On the other hand, for the hiding capacity, the size of information to be hidden relatively
depends to the size of the cover-image. The message size must be smaller than the image.
A large capacity allows the use of the smaller cover-image for the message of fixed size,
and thus decreases the bandwidth required to transmit the stego-image.
Another weakness is an attacker can easily destruct the message by removing or zeroing
the entire LSB plane with very little change in the perceptual quality of the modified stego-
image. Therefore, if this method causes someone to suspect something hidden in the stego-
image, then the method is not success.

43. CHAPTER 3
PROJECT DESIGN

44. 3.1) WORK BREAKDOWN STRUCTURE

45. 3.2) INTERFACE DESIGN
The interface is designed keeping the user in mind. There are two buttons on the left panel,
with one asking the user to load the file to be encrypted, and the other asking the user to
load the image in which the data is to be hidden. Only after the two files are browsed, will
the encryption start, or else it’ll give an error message asking the user to load both the files.
After the user clicks on the encryption button, the status bar at the bottom of the application
gives us the state of encryption and after the encryption is complete, we get a message box
stating the same. But before encryption is done, it’ll ask the user to give the name by which
the used would like to save the encrypted image which should also necessarily be filled.

46. CHAPTER 4
PROJECT
IMPLEMETATION

47. 4.1) Coding: The algorithm implementation of steganography in C# on Visual Studio.
4.2) SNAPSHOTS
The snapshots of the project are given below:
a) Interface in the beginning:

48. b) Interface with the second animation sequence:

49. c) Interface with the third animation sequence:

50. d) Interface with the fourth animation sequence:

51. e) Browsing the bitmap image file which will serve as a carrier:

52. f) Browsing the (*.txt) file which is to be encrypted:

53. g) Naming the new encrypted file and its location:

54. h) After the file, image and the destination location of the encrypted file:

55. i) ”Encrypted image has been savedsuccessfully!” Message Box:
4.3) Testing:
Functionality testing: testing if the end product is fully functional with all its functionality
intact.
Usability testing: testing if the application works fine with all its upload links and buttons
working.
Interface testing: testing if the interface is use friendly or not.
Compatibility testing: testing its compatibility with other systems or operating systems.

56. CHAPTER 5
PROJECT
AND
FUTURE SCOPE
5.1) PROJECT SCOPE

57. This application would enable defence personnels to send confidential data of high priority
to others. Because the human eye can not decipher that there is any encrypted text, it can
be put to great use while remaining easy to understand and use.
5.2) FUTURE SCOPE
In today’s world, we often listen a popular term “Hacking”. Hacking is nothing but an
unauthorized access of data which can be collected at the time of data transmission. With
respect to steganography this problem is often taken as Steganalysis. Steganalysis is a
process in which a steganalyzer cracks the cover object to get the hidden data. So, whatever
be the technique will be developed in future, degree of security related with that has to be
kept in mind. It is hoped that Dual Steganography, Steganography along with
Cryptography may be some of the future solution for this above mentioned problem.
We hope to add support to hide all file formats. This allows for a much broader spectrum
of uses: one would be able to encode .gif, .png, .pdf, .mp3, etc. The program would be
more versatile because often hiding text just isn’t enough. We also would like to implement
batch image processing and statistical analysis so that we can run the program through a
dataset of images and detect Steganography and perhaps crawl through Google Image
Search to see how prevalent Steganography is. We eventually plan to port the program to
use C/C++ so that we may take advantage of bit-fields in C and learn to code GUI’s as
well. I have a plug-in handler developed for C++ that I would like to use in this project so
that third-party developers may contribute to the project.

58. CHAPTER 6
CONCLUSION
6.1) CONCLUSION

59. As steganography becomes more widely used in computing, there are issues that need to
be resolved. There are a wide variety of different techniques with their own advantages and
disadvantages. Many currently used techniques are not robust enough to prevent detection
and removal of embedded data. The use of benchmarking to evaluate techniques should
become more common and a more standard definition of robustness is required to help
overcome this. For a system to be considered robust it should have the following properties:
The quality of the media should not noticeably degrade upon addition of a secret data.
Secret data should be undetectable without secret knowledge, typically the key.
If multiple data are present they should not interfere with each other.
The secret data should survive attacks that don’t degrade the perceived quality of the work.
This work presents a scheme that can transmit large quantities of secret information and
provide secure communication between two communication parties. Both steganography
and cryptography can be woven into this scheme to make the detection more complicated.
Any kind of text data can be employed as secret msg. The secret message employing the
concept of steganography is sent over the network. In addition, the proposed procedure is
simple and easy to implement. Also, the developed system has many practical, personal
and militaristic applications for both point-to-point and point-to multi- point
communications.

60. BIBLIOGRAPHY
BIBLIOGRAPHY
http://www.engpaper.com/free-research-papers-steganography.htm

61. http://en.wikipedia.org/wiki/Steganography
www.ece.stevens-tech.edu/~mouli/lsbsteg.pdf
www.waset.org/journals/waset/v50/v50-74.pdf
mo.co.za/open/stegoverview.pdf
www.maths.nuigalway.ie/cstudents/mcomms/.../steganography.pdf
http://www.ijcaonline.org/archives/volume6/number2/1057-1378
ipublishing.co.in/jarvol1no12010/EIJAER1018.pdf
faculty.ksu.edu.sa/ghazy/Steg/References/ref26-2.pdf
www1.chapman.edu/~nabav100/.../ImageSteganography.pdf
Kesslet, Gary C. An Overview of Steganography for the Computer Forensics
Examiner, Burlington, 2004.
Hosmer, Chet. Discovering Hidden Evidence, Cortland, 2006.
N.F. Johnson, S. Jajodia, “Staganalysis: The Investigation of Hiding
Information”, IEEE, pp. 113-116, 1998.
N.F. Johnson & S. Jajodia, “Steganalysis of Images Created Using Current
Steganography Software”, in Proceeding for the Second Information Hiding
Workshop, Portland Oregon, USA, April 1998, pp. 273-289.

62. APPENDIX
(PROGRAM CODE)
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using System.IO;
namespace Minor_Project
{

63. public partial class Steganography : Form
{
public Steganography()
{
InitializeComponent();
}
//public values:
string loadedTrueImagePath, loadedFilePath, saveToImage, DLoadImagePath,
DSaveFilePath;
int height, width;
long fileSize, fileNameSize;
Image loadedTrueImage, DecryptedImage, AfterEncryption;
Bitmap loadedTrueBitmap, DecryptedBitmap;
Rectangle previewImage = new Rectangle(370, 170, 400, 400);
bool canPaint = false, EncriptionDone = false;
byte[] fileContainer;
private void EnImageBrowse_btn_Click(object sender, EventArgs e)
{
if (openFileDialog1.ShowDialog() == DialogResult.OK)
{
loadedTrueImagePath = openFileDialog1.FileName;
EnImage_tbx.Text = loadedTrueImagePath;
loadedTrueImage = Image.FromFile(loadedTrueImagePath);
height = loadedTrueImage.Height;
width = loadedTrueImage.Width;
loadedTrueBitmap = new Bitmap(loadedTrueImage);
canPaint = true;
this.Invalidate();
}
}
private string smalldecimal(string inp, int dec)
{
int i;
for (i = inp.Length - 1; i > 0; i--)
if (inp[i] == '.')
break;
try
{
return inp.Substring(0, i + dec + 1);
}
catch
{
return inp;
}
}
private void EnFileBrowse_btn_Click(object sender, EventArgs e)
{
if (openFileDialog2.ShowDialog() == DialogResult.OK)
{
loadedFilePath = openFileDialog2.FileName;

64. EnFile_tbx.Text = loadedFilePath;
FileInfo finfo = new FileInfo(loadedFilePath);
fileSize = finfo.Length;
fileNameSize = justFName(loadedFilePath).Length;
}
}
private void Encrypt_btn_Click(object sender, EventArgs e)
{
if (saveFileDialog1.ShowDialog() == DialogResult.OK)
{
saveToImage = saveFileDialog1.FileName;
}
else
return;
if (EnImage_tbx.Text == String.Empty || EnFile_tbx.Text ==
String.Empty)
{
MessageBox.Show("Encrypton information is incomplete!nPlease
complete them first.", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
if (8 * ((height * (width / 3) * 3) / 3 - 1) < fileSize +
fileNameSize)
{
MessageBox.Show("File size is too large!nPlease use a larger
image to hide this file.", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
fileContainer = File.ReadAllBytes(loadedFilePath);
EncryptLayer();
}
private void EncryptLayer()
{
toolStripStatusLabel1.Text = "Encrypting... Please wait!";
Application.DoEvents();
long FSize = fileSize;
Bitmap changedBitmap = EncryptLayer(8, loadedTrueBitmap, 0, (height *
(width/3)*3) / 3 - fileNameSize - 1, true);
FSize -= (height * (width / 3) * 3) / 3 - fileNameSize - 1;
if(FSize > 0)
{
for (int i = 7; i >= 0 && FSize > 0; i--)
{
changedBitmap = EncryptLayer(i, changedBitmap, (((8 - i) *
height * (width / 3) * 3) / 3 - fileNameSize - (8 - i)), (((9 - i) * height *
(width / 3) * 3) / 3 - fileNameSize - (9 - i)), false);
FSize -= (height * (width / 3) * 3) / 3 - 1;
}
}
changedBitmap.Save(saveToImage);
toolStripProgressBar1.Increment(100);

65. toolStripStatusLabel1.Text = "Encrypted image has been saved
successfully.";
MessageBox.Show("Encrypted image has been saved successfully!",
"Done!", MessageBoxButtons.OK, MessageBoxIcon.Information);
EncriptionDone = true;
AfterEncryption = Image.FromFile(saveToImage);
this.Invalidate();
}
private Bitmap EncryptLayer(int layer, Bitmap inputBitmap, long
startPosition, long endPosition, bool writeFileName)
{
Bitmap outputBitmap = inputBitmap;
layer--;
int i = 0, j = 0;
long FNSize = 0;
int count = 0;
bool[] t = new bool[8];
bool[] rb = new bool[8];
bool[] gb = new bool[8];
bool[] bb = new bool[8];
Color pixel = new Color();
byte r, g, b;
if (writeFileName)
{
FNSize = fileNameSize;
string fileName = justFName(loadedFilePath);
//write fileName:
for (i = 0; i < height && i * (height / 3) < fileNameSize; i++)
for (j = 0; j < (width / 3) * 3 && i * (height / 3) + (j / 3)
< fileNameSize; j++)
{
byte2bool((byte)fileName[i * (height / 3) + j / 3], ref
t);
pixel = inputBitmap.GetPixel(j, i);
r = pixel.R;
g = pixel.G;
b = pixel.B;
byte2bool(r, ref rb);
byte2bool(g, ref gb);
byte2bool(b, ref bb);
if (gb[0] == true && gb[1] == true && gb[2] == true &&
gb[3] == true && bb[0] == true && bb[1] == true && bb[2] == true)
{
gb[4] = t[0];
gb[5] = t[1];
gb[6] = t[2];
gb[7] = t[3];
bb[5] = t[4];

66. bb[6] = t[5];
bb[7] = t[6];
rb[7] = t[7];
}
else if (gb[0] == true && gb[1] == true && gb[2] == true
&& bb[0] == true && bb[1] == true && bb[2] == true && bb[3] == true)
{
gb[5] = t[0];
gb[6] = t[1];
gb[7] = t[2];
bb[4] = t[3];
bb[5] = t[4];
bb[6] = t[5];
bb[7] = t[6];
rb[7] = t[7];
}
Color result = Color.FromArgb((int)bool2byte(rb), (int)bool2byte(gb),
(int)bool2byte(bb));
outputBitmap.SetPixel(j, i, result);
}
i--;
}
//write file (after file name):
int tempj = j;
for (; i < height && i * (height / 3) < endPosition - startPosition +
FNSize && startPosition + i * (height / 3) < fileSize + FNSize; i++)
for (j = 0; j < (width / 3) * 3 && i * (height / 3) + (j / 3) <
endPosition - startPosition + FNSize && startPosition + i * (height / 3) + (j / 3)
< fileSize + FNSize; j++)
{
if (tempj != 0)
{
j = tempj;
tempj = 0;
}
byte2bool((byte)fileContainer[startPosition + i * (height / 3)
+ j / 3 - FNSize], ref t);
pixel = inputBitmap.GetPixel(j, i);
r = pixel.R;
g = pixel.G;
b = pixel.B;
byte2bool(r, ref rb);
byte2bool(g, ref gb);
byte2bool(b, ref bb);
if (gb[0] == true && gb[1] == true && gb[2] == true && gb[3]
== true && bb[0] == true && bb[1] == true && bb[2] == true)
{
gb[4] = t[0];
gb[5] = t[1];

67. gb[6] = t[2];
gb[7] = t[3];
bb[5] = t[4];
bb[6] = t[5];
bb[7] = t[6];
rb[7] = t[7];
}
else if (gb[0] == true && gb[1] == true && gb[2] == true &&
bb[0] == true && bb[1] == true && bb[2] == true && bb[3] == true)
{
gb[5] = t[0];
gb[6] = t[1];
gb[7] = t[2];
bb[4] = t[3];
bb[5] = t[4];
bb[6] = t[5];
bb[7] = t[6];
rb[7] = t[7];
}
Color result = Color.FromArgb((int)bool2byte(rb), (int)bool2byte(gb),
(int)bool2byte(bb));
outputBitmap.SetPixel(j, i, result);
}
long tempFS = fileSize, tempFNS = fileNameSize;
r = (byte)(tempFS % 100);
tempFS /= 100;
g = (byte)(tempFS % 100);
tempFS /= 100;
b = (byte)(tempFS % 100);
Color flenColor = Color.FromArgb(r, g, b);
outputBitmap.SetPixel(width - 1, height - 1, flenColor);
r = (byte)(tempFNS % 100);
tempFNS /= 100;
g = (byte)(tempFNS % 100);
tempFNS /= 100;
b = (byte)(tempFNS % 100);
Color fnlenColor = Color.FromArgb(r, g, b);
outputBitmap.SetPixel(width - 2, height - 1, fnlenColor);
return outputBitmap;
}
private void DecryptLayer()
{
toolStripStatusLabel1.Text = "Decrypting... Please wait";
Application.DoEvents();
int i, j = 0;

68. bool[] t = new bool[8];
bool[] rb = new bool[8];
bool[] gb = new bool[8];
bool[] bb = new bool[8];
Color pixel = new Color();
byte r, g, b;
pixel = DecryptedBitmap.GetPixel(width - 1, height - 1);
long fSize = pixel.R + pixel.G * 100 + pixel.B * 10000;
pixel = DecryptedBitmap.GetPixel(width - 2, height - 1);
long fNameSize = pixel.R + pixel.G * 100 + pixel.B * 10000;
byte[] res = new byte[fSize];
string resFName = "";
byte temp;
//Read file name:
for (i = 0; i < height && i * (height / 3) < fNameSize; i++)
for (j = 0; j < (width / 3) * 3 && i * (height / 3) + (j / 3) <
fNameSize; j++)
{
pixel = DecryptedBitmap.GetPixel(j, i);
r = pixel.R;
g = pixel.G;
b = pixel.B;
byte2bool(r, ref rb);
byte2bool(g, ref gb);
byte2bool(b, ref bb);
if (gb[0] == true && gb[1] == true && gb[2] == true && gb[3]
== true && bb[0] == true && bb[1] == true && bb[2] == true)
{
t[0] = gb[4];
t[1] = gb[5];
t[2] = gb[6];
t[3] = gb[7];
t[4] = bb[5];
t[5] = bb[6];
t[6] = bb[7];
t[7] = rb[7];
temp = bool2byte(t);
resFName += (char)temp;
}
else if (gb[0] == true && gb[1] == true && gb[2] == true &&
bb[0] == true && bb[1] == true && bb[2] == true && bb[3] == true)
{
t[0] = gb[5];
t[1] = gb[6];
t[2] = gb[7];
t[3] = bb[4];
t[4] = bb[5];
t[5] = bb[6];
t[6] = bb[7];
t[7] = rb[7];

69. temp = bool2byte(t);
resFName += (char)temp;
}
}
//Read file on layer 8 (after file name):
int tempj = j;
i--;
for (; i < height && i * (height / 3) < fSize + fNameSize; i++)
for (j = 0; j < (width / 3) * 3 && i * (height / 3) + (j / 3) <
(height * (width / 3) * 3) / 3 - 1 && i * (height / 3) + (j / 3) < fSize +
fNameSize; j++)
{
if (tempj != 0)
{
j = tempj;
tempj = 0;
}
pixel = DecryptedBitmap.GetPixel(j, i);
r = pixel.R;
g = pixel.G;
b = pixel.B;
byte2bool(r, ref rb);
byte2bool(g, ref gb);
byte2bool(b, ref bb);
if (gb[0] == true && gb[1] == true && gb[2] == true && gb[3]
== true && bb[0] == true && bb[1] == true && bb[2] == true)
{
t[0] = gb[4];
t[1] = gb[5];
t[2] = gb[6];
t[3] = gb[7];
t[4] = bb[5];
t[5] = bb[6];
t[6] = bb[7];
t[7] = rb[7];
temp = bool2byte(t);
res[i * (height / 3) + j / 3 - fNameSize] = temp;
}
else if (gb[0] == true && gb[1] == true && gb[2] == true &&
bb[0] == true && bb[1] == true && bb[2] == true && bb[3] == true)
{
t[0] = gb[5];
t[1] = gb[6];
t[2] = gb[7];
t[3] = bb[4];
t[4] = bb[5];
t[5] = bb[6];
t[6] = bb[7];
t[7] = rb[7];

70. temp = bool2byte(t);
res[i * (height / 3) + j / 3 - fNameSize] = temp;
}
}
//Read file on other layers:
long readedOnL8 = (height * (width / 3) * 3) / 3 - fNameSize - 1;
for (int layer = 6; layer >= 0 && readedOnL8 + (6 - layer) * ((height * (width /
3) * 3) / 3 - 1) < fSize; layer--)
for (i = 0; i < height && i * (height / 3) + readedOnL8 + (6 -
layer) * ((height * (width / 3) * 3) / 3 - 1) < fSize; i++)
for (j = 0; j < (width / 3) * 3 && i * (height / 3) + (j / 3)
+ readedOnL8 + (6 - layer) * ((height * (width / 3) * 3) / 3 - 1) < fSize; j++)
{
pixel = DecryptedBitmap.GetPixel(j, i);
r = pixel.R;
g = pixel.G;
b = pixel.B;
byte2bool(r, ref rb);
byte2bool(g, ref gb);
byte2bool(b, ref bb);
if (gb[0] == true && gb[1] == true && gb[2] == true &&
gb[3] == true && bb[0] == true && bb[1] == true && bb[2] == true)
{
t[0] = gb[4];
t[1] = gb[5];
t[2] = gb[6];
t[3] = gb[7];
t[4] = bb[5];
t[5] = bb[6];
t[6] = bb[7];
t[7] = rb[7];
temp = bool2byte(t);
res[i * (height / 3) + j / 3 + (6 - layer) * ((height
* (width / 3) * 3) / 3 - 1) + readedOnL8] = temp;
}
else if (gb[0] == true && gb[1] == true && gb[2] == true
&& bb[0] == true && bb[1] == true && bb[2] == true && bb[3] == true)
{
t[0] = gb[5];
t[1] = gb[6];
t[2] = gb[7];
t[3] = bb[4];
t[4] = bb[5];
t[5] = bb[6];
t[6] = bb[7];
t[7] = rb[7];
temp = bool2byte(t);
res[i * (height / 3) + j / 3 + (6 - layer) * ((height
* (width / 3) * 3) / 3 - 1) + readedOnL8] = temp;
}
}
if (File.Exists(DSaveFilePath + "" + resFName))
{

71. MessageBox.Show("File "" + resFName + "" already exist please
choose another path to save file", "Error", MessageBoxButtons.OK,
MessageBoxIcon.Error);
return;
}
else
File.WriteAllBytes(DSaveFilePath + "" + resFName, res);
toolStripStatusLabel1.Text = "Decrypted file has been successfully
saved.";
Application.DoEvents();
}
private void byte2bool(byte inp, ref bool[] outp)
{
if(inp>=0 && inp<=255)
for (short i = 7; i >= 0; i--)
{
if (inp % 2 == 1)
outp[i] = true;
else
outp[i] = false;
inp /= 2;
}
else
throw new Exception("Input number is illegal.");
}
private byte bool2byte(bool[] inp)
{
byte outp = 0;
for (short i = 7; i >= 0; i--)
{
if (inp[i])
outp += (byte)Math.Pow(2.0, (double)(7-i));
}
return outp;
}
private void Decrypt_btn_Click(object sender, EventArgs e)
{
if (DeSaveFile_tbx.Text == String.Empty || DeLoadImage_tbx.Text ==
String.Empty)
{
MessageBox.Show("Text boxes must not be empty!", "Error",
MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
if (System.IO.File.Exists(DeLoadImage_tbx.Text) == false)
{
MessageBox.Show("Select image file.", "Error",
MessageBoxButtons.OK, MessageBoxIcon.Exclamation);
DeLoadImage_tbx.Focus();
return;
}

72. DecryptLayer();
}
private void DeLoadImageBrowse_btn_Click(object sender, EventArgs e)
{
if (openFileDialog3.ShowDialog() == DialogResult.OK)
{
DLoadImagePath = openFileDialog3.FileName;
DeLoadImage_tbx.Text = DLoadImagePath;
DecryptedImage = Image.FromFile(DLoadImagePath);
height = DecryptedImage.Height;
width = DecryptedImage.Width;
DecryptedBitmap = new Bitmap(DecryptedImage);
canPaint = true;
this.Invalidate();
}
}
private void DeSaveFileBrowse_btn_Click(object sender, EventArgs e)
{
if (folderBrowserDialog1.ShowDialog() == DialogResult.OK)
{
DSaveFilePath = folderBrowserDialog1.SelectedPath;
DeSaveFile_tbx.Text = DSaveFilePath;
}
}
private void Form1_Paint(object sender, PaintEventArgs e)
{
if(canPaint)
try
{
if (!EncriptionDone)
e.Graphics.DrawImage(loadedTrueImage, previewImage);
else
e.Graphics.DrawImage(AfterEncryption, previewImage);
}
catch
{
e.Graphics.DrawImage(DecryptedImage, previewImage);
}
}
private string justFName(string path)
{
string output;
int i;
if (path.Length == 3) // i.e: "C:"
return path.Substring(0, 1);
for (i = path.Length - 1; i > 0; i--)
if (path[i] == '')
break;
output = path.Substring(i + 1);
return output;

73. }
private string justEx(string fName)
{
string output;
int i;
for (i = fName.Length - 1; i > 0; i--)
if (fName[i] == '.')
break;
output = fName.Substring(i + 1);
return output;
}
private void Close_btn_Click(object sender, EventArgs e)
{
this.Close();
}
}
}