COMPARISON_OF_DATA_HIDING_USING_LSB_AND.pdf

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COMPARISON OF DATA HIDING USING LSB AND DCT FOR IMAGE.
ZAMZAM HOHAMED AHMED
A project report submitted in partial
fulfillment of the requirement for the award of the
Degree of Master of Computer Science (Information Security)
Faculty of Computer Science and Information Technology
Universiti Tun Hussein Onn Malaysia
DECEMBER 2014

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ABSTRACT
Steganography is the art and science of hiding information by embedding data into cover
media. There are two main domains namely spatial and frequency domain. In this
project, a comparison is made between hiding in spatial domain using LSB technique
and hiding in frequency domain using DCT technique. Various BMP cover images with
256x256 and 512x512 resolutions for LSB and DCT techniques are used in the
experimentations. For DCT technique, various BMP image is converted to JPEG image.
Then data is hidden into the JPEG cover image using DCT technique. After that, the
JPEG stego image is converted back to BMP stego image. These BMP stego images
from LSB and DCT are compared using PSNR. Results from these experiments show
that 75% of the stego images hidden using LSB has shown higher PSNR values than
stego images hidden using DCT. This means that the stego image hidden using LSB has
shown a much closer similarity to the cover image than stego image hidden using DCT,
thus much harder to detect hidden data in the stego image by LSB.

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ABSTRAK
Steganografi adalah seni dan sains menyembunyikan maklumat dengan memasukkan
data ke dalam cover media. Terdapat dua domain utama iaitu domain spatial dan
frequency. Dalam projek ini, perbandingan yang dibuat antara menyembunyikan data di
dalam domain spatial menggunakan teknik LSB dan menyembunyikan data di dalam
domain frekuensi menggunakan teknik DCT. Pelbagai cover image BMP dengan
resolusi 256x256 dan 512x512 untuk teknik LSB dan teknik DCT digunakan dalam
eksperimen. Untuk teknik DCT, imej BMP ditukar kepada imej JPEG. Kemudian data
disembunyikan ke dalam JPEG menggunakan teknik DCT. Selepas itu, imej stego JPEG
ditukar kembali ke imej stego BMP. Imej stego BMP dari LSB dan DCT dibanding
menggunakan PSNR. Hasil daripada eksperimen ini menunjukkan bahawa 75% daripada
imej stego yang disembunyikan data menggunakan LSB telah menunjukkan nilai PSNR
yang lebih tinggi daripada imej stego yang disembunyikan data menggunakan DCT. Ini
bermakna bahawa imej stego menggunakan LSB telah menunjukkan persamaan yang
lebih dekat dengan cover image daripada imej stego yang disembunyi menggunakan
DCT, oleh itu jauh lebih sukar untuk mengesan data yang tersembunyi dalam imej stego
dengan LSB.

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TABLE OF CONTENTS
TITLE i
DECLARATION ii
DEDICATION iv
ACKNOWLEDGEMENT v
ABSTRACT vi
ABSTRAK vii
TABLE OF CONTENTS viii
LIST OF TABLES xi
LIST OF FIGURES xii
ABBREVIATIONS xiii
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.1 History of steganography 2
1.2 Problem statement 2
1.3 Objectives 3
1.4 Scope 3
1.5 Dissertation outline 4
CHAPTER 2 LITERATURE REVIEW 5
2.1 The basic framework of steganography 5
2.2 The purpose of steganography 7
2.3 Types of steganography 7
2.3.1 Text steganography 8
2.3.2 Audio steganography 9

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2.3.3 Image steganography 9
2.4 Image format 9
2.4.1 Tiff file 10
2.4.2 Gif file 10
2.4.3 Bmf file 10
2.4.4 Jpeg file 11
2.5 Image compression 11
2.5.1 Jpeg compression 12
2.6 Steganography domain and technique 12
2.6.1 Spacial domain 13
2.6.1.1 Least significant bit Technique 13
2.6.1.1.1 Lsb algorithm 15
2.6.2 Frequency domain 16
2.6.2.1 Discrete cosine transform technique 16
2.6.2.2 Discrete wavelet transform 18
2.7 Peak signal to noise ratio (PSNR) 18
2.8 Summary 19
CHAPTER 3 METHODOLOGY 20
3.1 The proposed method 20
3.1.1 Proposed framework 21
3.2 Least significant bit technique 22
3.3 Discrete cosine transform technique 24
3.4 Comparison and measurement 25
2.5 Summary 26
CHAPTER 4 IMPLEMENTATION 27
4.1 Introduction 27
4.2 Least significant bit technique 27
4.2.1 Lsb embedding algorithm in matlab 28
4.2.2 Lsb extracting algorithm in matlab 29
4.3 Discrete cosine transform technique 29
4.3.1 Dct embedding algorithm in matlab 30

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4.3.2 Dct extracting algorithm in matlab 33
4.4 The peak signal to noise ratio for Dct and lsb 35
4.5 Summary 36
CHAPTER 5 RESULT AND DISCUSSION 37
5.1 Introduction 37
5.2 Least significant bit (LSB) technique 37
5.2.1 Lsb embedding algorithm result 38
5.2.2 Lsb extracting algorithm result 39
5.3 Discrete cosine transform (DCT) technique 40
5.3.1 Dct embedding algorithm result 40
5.3.2 Dct Extracting algorithm result 41
5.4 Comparison and measurement 42
5.4.1 Analysis of test result for lsb 47
5.4.2 Analysis of test result for dct 48
5.4.3 Analysis of test result for lsb and dct 49
5.5 Summary 51
CHAPTER 6 CONCLUSION 52
6.1 Conclusion 52
6.2 Future work 53
REFERENCES 54
APPENDIX 57

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LIST OF TABLES
5.1 BMP images using LSB 43
5.2 BMPimages using DCT 44
5.3 LSB stego images 256x256 45
5.4 LSB stego images 512x512 45
5.5 DCT stego images 256x256 46
5.6 DCT stego images 512x512 46
5.7 Summary of the results 50

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LIST OF FIGURES
2.1 The different embodiment disciplines of information hiding 6
2.2 General model of Steganography 8
2.3 Algorithm of Least Significant Bit 15
2.4 Block diagram of JPEG encoder and decoder 17
2.5 One Dimensional DCT 17
2.6 Two Dimensional DCT 18
3.1 Proposed Framework 21
3.2 LSB embedding algorithm 23
3.3 LSB extraction algorithm 23
3.4 DCT based embedding algorithm 24
3.5 DCT based extraction algorithm 25
4.1 LSB based embedding implementation code in Matlab 28
4.2 LSB based extracting implementation code in Matlab 29
4.3 DCT based embedding implementation code in Matlab 31
4.4 DCT based extracting implementation code in Matlab 34
4.5 Matlab code for PSNR of the LSB stego image 35
4.6 Matlab code for PSNR of the DCT stego image 36
5.1 The result obtained by running LSB embedding algorithm 38
5.2 The result obtained by running LSB extraction algorithm 39
5.3 The result obtained by running DCT embedding algorithm 40
5.4 The result obtained by running DCT extracting algorithm 41
5.5 LSB stego images 512x512 and 256x256 47
5.6 DCT stego images 512x512 and 256x256 48
5.7 Stego images 512x512 resolutions for LSB and DCT 49
5.8 Stego images 256x256 resolutions for LSB and DCT 50

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LIST OF ABBREVIATIONS
LSB Least Significant Bit
MSB Most Significant Bit
DCT Discrete Cosine Transform
DWT Discrete Wavelet Transform
DFT Discrete Fourier Transform
PSNR Peak Signal-to-Noise Ratio
MSE Mean Squared Error
HVS Human Visibility System
TIFF Tagged Image Format File
GIF Graphic Interchange Format
BMP Bitmap
JPEG Joint Photographic Experts Group
ASCII American Standard Code for Information
Interchange
2D-DCT Two-Dimensional Discrete Cosine Transform

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CHAPTER 1
INTRODUCTION
1.1 Background
Steganography is the art and science of communicating in a way that the presence of a
secret message apart from the identity of the sender and intended recipient cannot be
detected by unauthorized users. Steganography is a technique which is used to hide a
secret message within a cover media in such a way that others cannot detect the presence
of the hidden message. Steganography is made from the Greek words steganos meaning
"covered or protected" and graphei meaning "writing". While classical cryptography is
about concealing the content of messages, steganography is about concealing their
existence.

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1.1.1 History of Steganography
Steganography goes back to ancient times and used by different cultures such as:
Greeks, Chinese, and medieval Europe. A famous which case dates back to 1586, when
Mary Queen of Scots was conspiring to have Queen Elizabeth of England assassinated,
with a view to taking over the English throne [1]. Also during the 1980’s, Margaret
Thatcher became so irritated at press leaks of cabinet documents that she had the word
processors programmed to encode their identity in the word spacing, so that disloyal
ministers could be traced [2]. Similar techniques are now undergoing trials in an
electronic publishing project, with a view to hiding copyright messages and serial
numbers in documents. In some applications, it is enough to hide the identity of either
the sender or the recipient of the message, rather than its very existence [3]. Modern
steganography entered the world in 1985 with the advent of the personal computer being
applied to classical steganography problems. Also in modern Steganography practice the
larger the cover message is relative to the hidden message, the easier it is to hide the
latter [4].
1.2 Problem Statement
Today the growth in the information technology, especially in computer networks such
as Internet, Mobile communication, and Digital Multimedia applications has opened
new opportunities in scientific and commercial applications. But this progress has also
led to many serious problems such as hacking, duplications and malicious usage of
digital information. Usually message such as pictures can be altered by unauthorized
user between the sender and the receiver of the message so that the receiver of the
message cannot verify the integrity of the message which compromises one of the goals
of information security. With the help of steganography messages can be hidden inside
the digital media /cover object for transmission, because steganography can be applied

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differently in digital image, audio and video file, it’s difficult to detect, only receiver can
detect it and it can be done faster with the large number of softwares.
1.3 Objectives
The Objectives of this project is to:
1. To implement steganography images based on Least Significant Bit (LSB) and
Discrete Cosine Transform (DCT) techniques in Matlab
2. To test these techniques using cover images with 256x256 and 512x512 resolutions
3. To compare and analyze using Peak Signal to Noise Ratio (PSNR)
1.4 Scope
The scope of this research is:
1. Hiding data in image steganography
2. Using Least Significant Bit (LSB) and Discrete Cosine Transform (DCT)
3. Do testing on cover images with 256x256 and 512x512 resolutions only
4. BMP file format is chosen to be used in this dissertation due to their higher
resolution compared to other images. The BMP files are uncompressed, hence they
are large. The advantage of using BMP files is simplicity and wide acceptance of
BMP files in windows programs
5. To make apple-to-apple comparison of LSB and DCT, BMP is converted to JPEG
inorder to use DCT technique. the JPEG stego is then converted back to BMP stego.
These BMP tego from LSB and DCT are then compared using PSNR

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1.5 Dissertation Outline
The organization of this dissertation is as follows: Chapter 2 describes the different types
of steganography and reviews of the existing researches related to steganography
techniques. Chapter 3 describes the proposed framework to embed the secret message
inside the steganography images and extract the hiding data back from the stego images.
Chapter 4 describes the implementation of the proposed algorithm. Chapter 5 discusses
and analyze the experimental result of the approaches and its performance comparison.
Finally, Chapter 6 describes conclusion and future work of the dissertation.

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CHAPTER 2
LITERATURE REVIEW
2.1 The Basic Framework of Steganography
An Example of steganography can be given in terms of communication between two
people, Alice and Bob, where Alice and Bob are two inmates who wish to communicate
in order to exchange some secret information. However, all communication between
them is examined by the eavesdropper, Wendy, the third party who will try hardly to
disclose, alter and/or destruct their secret message.
Specifically, in the general model for steganography, illustrated in Figure 2.1, Alice
wishes to send a secret message m to Bob. In order to do so, she "embeds" m into a
cover-object c, and obtains a stego-object s. The stego-object s is then sent through the
public channel. Thus we have the following definitions:
i. Cover-object: is the object used as the carrier to embed messages into many
different objects have been employed to embed messages into for example
images, audio, and video as well as file structures, and html pages to name a few.
ii. Stego-key: is the code that the sender of the secret message is going to use to
embed the message into the cover-object. This same stego-key will be used by
the recipient to extract the secret message.

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iii. Stego-object: is the combination of the cover object, the stego-key and the secret
message.
In a pure steganography framework, the technique for embedding the message is
unknown to Wendy and shared as a secret between Alice and Bob. However, it is
generally considered that the algorithm in use is not secret but only the key used by the
algorithm is kept as a secret between the two parties, this assumption is also known as
Kerchoff's principle in the field of cryptography. The secret key, for example, can be a
password used to seed a pseudo-random number generator to select pixel locations in an
image cover-object for embedding the secret message (possibly encrypted). Hence, there
is a need for a cover media, stego function, stego- key and the secret message to be
hidden. The cover media can be a plaintext, still image, video and audio. Performing
data hiding in Image was studied in a wide variety of literatures.
Figure 2.1: General model of Steganography [5]

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2.2 The Purpose of Steganography
According to the major objective of steganography is to prevent some unintended
observer from stealing or destroying the confidential information. There are some
factors to be considered when designing a steganography system: [7]
i. Invisibility: Invisibility is the ability to be unnoticed by the human.
ii. Security: Even if an attacker realizes the existence of the information in the stego
object it should be impossible for the attacker to detect the information. The
closer the stego image to the cover image, the higher the security. It is measured
in terms of Peak Signal to Noise Ratio (PSNR).
2.3 Types of Steganography
The four main types of Steganography are digital image steganography, audio
steganography, text steganography and video steganography. Figure 2.2 shows the
overall structure of System Security which basically consists of Cryptography and
Information Hiding which has been divided also into another two categories which are
Watermarking and Steganography. This study will focus on information hiding using
digital image steganography.

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A cover object is the object designated to carry the embedded bits or secret information.
The cover objects can be a text file, image file, audio file or video file. These different
type of steganography with various cover objects are discussed further in the following
sections.
2.3.1 Text Steganography
Historically hiding information in the text was a simple and the most important method
of steganography but Due to the beginning of the Internet and due to the different type
of digital file formats it has decreased in importance. Text steganography using digital
files is not used very often because the text files have a very small amount of redundant
data [8].
Figure 2.2: The different embodiment disciplines of information hiding [6].

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2.3.2 Audio Steganography
Audio steganography is masking, which exploits the properties of the human ear to hide
information unnoticeably. An audible, sound becomes inaudible in the presence of
another louder audible sound. This property allows the selection of the channel in which
the information will be hidden. Although it is similar to images in steganographic
potential, the larger size of meaningful audio files makes them less likely to use than
images [8].
2.3.3 Image Steganography
Image Steganography will be used as cover object or host image for this project because
Images are considered as the most popular file formats used in steganography. They are
known for constituting a non-causal medium, due to the possibility to access any pixel of
the image at random. In addition, the hidden information could remain invisible to the
eye. However, the image steganography techniques will exploit "holes" in the Human
Visual System (HVS) [8] [9].
2.4 Image Format
this research fucusses on some specific image formats. The followings are the formats
that this research fucuss on.

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2.4.1 TIFF File
Tagged Image Format File (TIFF) is an image format file for high quality graphics. TIFF
files were created in the 1986 as a file format for scanned imaged in an attempt to get all
companies to use one standard file format instead of multiple. Though TIF files
originally only supported black and white, the update in 1988 added a color palette [10].
2.4.2 GIF File
Graphics Interchange Format is used for the purpose of storing multiple bitmap images
in a single file for exchange between platforms and images. It is often used for storing
multibit graphics and image data. GIF is not associated with a particular software
application but was designed “to allow the easy interchange and viewing of image data
stored on local or remote computer systems” [10] [11].
2.4.3 BMP File
The letters “BMP” stand for “bitmap”, Bitmap images were introduced by Microsoft to
be a standard image file format between users of their Windows operating system. The
file format is now supported across multiple file systems and operating systems, but is
being used less and less often. A key reason for this is the large file size, resulting from
poor compression and verbose file format. This is, however, an advantage for hiding
data without raising suspicion. To understand how bitmap images can be used to conceal
data, the file format must first be explained. A bitmap file can be broken into two main
blocks, the header and the data. The header, which consists of 54 bytes, can be broken
into two sub-blocks. These are identified as the Bitmap Header, and the Bitmap

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Information. Images which are less than 16 bit have an additional sub-block within the
header labeled the Color Palette [12] [13].
2.4.4 JPEG File
Joint Photographic Experts Group (JPEG) format is one of the Transform Domain
Techniques which has an advantage over LSB techniques because they hide information
in areas of the image that are less exposed to compression, cropping, and image
processing [9]. Also JPEG is most common image file format on the internet owing to
the small size of resultant images obtained by using it, and it is efficient for appearing
the stage image to something similar to the original image [14].
2.5 Image Compression
When working with larger images of greater bit depth, the images tend to become too
large to be transmitted 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 analyze 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 [9].
i. Lossless compression is known for being preferable when the original data
should stay in its entirety. In this manner, the original image information will
never be removed, and this makes it possible the reconstruction of the original
data from the compressed data. This is typical of images in GIF and BMP.
ii. Lossy compression saves storage space by discarding the points the human eyes
find difficult to identify. In this case the resulting image is expected to be

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something similar to the original image, but not the same as the original. JPEG
compression uses this technique.
2.5.1 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. 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. JPEG images are the products of digital cameras, scanners, and other
photographic image capture devices. This is simply why concealing secret information
in JPEG images might provide a better disguise [9] [15].
For JPEG, the Discrete Cosine Transform (DCT) is used. It is important to 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 [16].
2.6 Steganography Domains and Techniques
Over the past years, many literatures discussed the technique of information hiding. Up
until now, there are two techniques developed in information hiding, spatial-domain
manner and frequency-domain manner. Owing to the fact that the media considered in
these literatures are image illustrations, we therefore will include images in our

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discussions. The so-called spatial-domain refers to the fact that the secret is mixed into
the distributed pixels (regions) directly. While in the frequency-domain, it is necessary
to transform the host-image first using a frequency-oriented mechanism, such as a
discrete cosine transformation based (DCT-based), wavelet-based, etc., after which the
secret is then combined with the relative coefficients in the frequency-form image. Let
us take another look at the spatial-domain manner. Generally speaking, it is simpler to
achieve the goal of information hiding in the course of secret embedding. The least
significant bit (LSB for short) secret embedding or LSB-like embedding is the most
commonly used method in the spatial-domain approach [17].
2.6.1 Spatial Domain
These techniques use the pixel gray levels and their color values directly for encoding
the message bits. These techniques are some of the simplest schemes in terms of
embedding and extraction complexity. The major drawback of these methods is amount
of additive noise that creeps in the image which directly affects the Peak Signal to Noise
Ratio and the statistical properties of the image. Moreover these embedding algorithms
are applicable mainly to lossless image-compression schemes like TIFF images. For
lossy compression schemes like JPEG, some of the message bits get lost during the
compression step. The most common algorithm belonging to this class of techniques is
the Least Significant Bit (LSB) replacement technique.
2.6.1.1 Least Significant Bit Technique
Popular steganographic tools based on LSB embedding vary on the existing approaches
for hiding information. Some algorithms change LSB of the pixels visited in a random
walk, others modify pixels in certain areas of images, or instead of just changing the last
bit they increment or decrement the pixel value. The concept of least significant bit

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(LSB) Embedding is simple. It exploits the fact that the level of precision in many image
formats is far greater than that perceivable by the standard of human vision. Therefore,
an altered image with slight variations in its colors will be indistinguishable from the
original by a human being, just by looking at it.
When using the least significant bit of the pixels' color data to store the hidden message,
the image itself is seemed unaltered. Modulating the least significant bit does not result
in human-perceptible difference because the amplitude of the change is small. To hide a
secret message inside an image, a proper cover image is needed. It is necessary to use a
lossless compression format, because this method uses bits of each pixel in the image,
otherwise the hidden information will get lost in the transformations of a lossy
compression algorithm. When using a 24-bit color image, a bit of each of the red, green
and blue color components can be used, so a total of 3 bits can be stored in each pixel.
For example, the following grid can be considered as 3 pixels of a 24-bit color image,
using 9 bytes of memory:
(00100111 11101001 11001000)
(00100111 11001000 11101001)
(11001000 00100111 11101001)
When the character A, which binary value equals 10000001, is inserted, the following
grid results:
(00100111 11101000 11001000)
(00100110 11001000 11101000)
(11001000 00100111 11101001)
In this case, only three bits are needed to be changed to insert the character successfully.
On average, only half of the bits in an image will be needed to be insider modified to
hide a secret message using the maximal cover size. The result changes that are made to
the least significant bit are too small to be recognized by the human visual system
(HVS), so the message is effectively hidden [18].
The least significant bit of the third color remains without any changes. It can be used
for checking the correctness of 8 bits which are embedded in these 3 pixels.

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2.6.1.1.1 LSB Algorithm
i. Select a cover image of size M*N as an input.
ii. The message to be hidden is embedded in RGB component only of an image.
iii. Use a pixel selection filter to obtain the best areas to hide information in the
cover image to obtain a better rate. The filter is applied to Least Significant Bit
(LSB) of every pixel to hide information, leaving most significant bits (MSB).
iv. After that Message is hidden using Bit Replacement method.
Figure 2.3: Algorithm of Least Significant Bit [19].
Message to be
Hidden
Carrier Image
RGB
Component
Pixel Filtering
Least Significant Bit
replacement method
Stego Image

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2.6.2 Frequency Domain
These techniques are applied in encoding message bits in the transform domain
coefficients of the image. Data embedding performed in the transform domain is widely
used for robust watermarking. Similar techniques can also realize large capacity
embedding for steganography. Candidate transforms include Discrete Cosine Transform
(DCT), Discrete Wavelet Transform (DWT), and Discrete Fourier Transform (DFT). By
being embedded in the transform domain, the hidden data resides in more robust areas,
spread across the entire image, and provides better resistance against signal processing.
For example, we can perform a block DCT and, depending on payload and robustness
requirements, choose one or more components in each block to form a new data group
that, in turn, is pseudo randomly scrambled and undergoes a second-layer
transformation. Modification is then carried out on the double transform domain
coefficients using various schemes. These techniques have high embedding and
extraction complexity. Because of the robustness properties of transform domain
embedding, these techniques are generally more applicable to the “Watermarking”
aspect of data hiding. Many steganographic techniques in these domain have been
inspired from their watermarking counterparts [20] [21].
2.6.2.1 Discrete Cosine Transform Technique
DCT is a method of hiding information in transform domain images. This method hides
messages in significant areas of the cover 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 as good as to distinguish

17
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
[15] [22].
Figure 2.4: Block diagram of JPEG encoder and decoder [15].
DCT Algorithm
a) The One-Dimensional DCT
n = size, p = pixel, G = coefficients
Figure 2.5: One Dimensional DCT [22].
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b) The Two-Dimensional DCT
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Figure 2.6: Two Dimensional DCT [22].
2.6.2.2 Discrete Wavelet Transform
Wavelets are special functions which (in a form analogous to sins and cosines in Fourier
analysis) are used as basal functions for representing signals. The simplest DWT is Haar.
In Haar-DWT the low frequency wavelet coefficient are generated by averaging the two
pixel values and high frequency coefficients are generated by taking half of the
difference of the same two pixels. A signal is passed through a series of filters to
calculate DWT [23].
2.7 Peak Signal to Noise Ratio (PSNR)
The Peak Signal to Noise Ratio (PSNR) is an engineering term for the well-known
objective image quality metrics that uses for image measuring. Where PSNR formula is:
[7] [24]

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 PSNR formula.
Where MAXI is the maximum possible pixel value of the image and MSE is Mean
Square Error.
The high PSNR value indicates high security because it indicates the minimum
difference between the original and stego values. So no one can suspect the hidden
information.
 Capacity: The amount of information that can be hidden relative to the size of the
cover object without deteriorating the quality of the cover object.
 Robustness: It is the ability of the stego to withstand manipulations such as
filtering, cropping, rotation and compression.
2.8 Summary
This chapter discusses basic frame work of steganography, the purpose os
steganography, types of steganography and different file formats of image
steganography. It also discusses in detail the steganography techniques, Least
Significant Bit (LSB) and Discrete Cosine Transform (DCT) and The Peak Signal to
Noise Ratio (PSNR) as a well-known objective image quality metrics that uses for image
measuring.

20
CHAPTER 3
METHODOLOGY
3.1 The Proposed Method
Comparison of Spatial domain technique with Frequency domain technique has been
proposed to analyze which technique is more secure by having a high Peak Signal to
Noise Ratio (PSNR). As we know the goal of Steganography is to secure
communications from an eavesdropper, steganographic techniques strive to hide the
very presence of the message itself from an observer. So in order to reach a high
security and advanced method the Least Significant Bit will be compared to the Discrete
Cosine Transform and the performance of the two techniques will be measured using the
Peak Signal to Noise Ratio (PSNR).

21
3.11 Proposed Framework
In this study data hiding using Least Significant Bit (LSB) technique in Spatial Domain
will be compared to data hiding using Discrete Cosine Transform (DCT) technique in
Frequency Domain and the cover object that will be used to hide the data is cover image.
Figure 3.1 below is showing the proposed framework for this dissertation.
Figure 3.1: Proposed Framework

22
As shown in Firgure 3.1. In order to exchange the secret message in a secure way that
prevent the eavesdropper from recognizing, hacking and/or altering the stego image and
the secret message the sender and the Receiver will use the Least Significant Bit (LSB)
technique and Discrete Cosine Transform (DCT) technique. The Sender wants to send a
secret message to the Receiver where the Attacker is the third part or the eavesdropper
who wants to attack the secret message in order to change according to his/her personal
proposes. To prevent the Attacker from succeeding with his/her mission and even
recognizing the stego image from the other images, the proposed framework has two
stages. In the first stage, the secret message is embedded in the BMP cover image by
using Least Significant Bit’s embedding algorithm. then, the stego image is sent by the
sender to the receiver where the receiver extracts the hiding data from the BMP stego
image by using Least Significant Bit’s extracting algorithm. In the second stage, the
BMP cover image is converted to JPEG and the secret message is embedded in the JPEG
cover image by using Discrete Cosine Transfrom’s embedding algorithm then, the JPEG
stego image is converted to BMP stego image and sent by the sender to the receiver
where the receiver extracts the hiding data from the JPEG stego image by coverting back
the BMP stego image into JPEG stego image and then he/she uses Discrete Cosine
Transfrom’s extracting algorithm. Both BMP stego images that produced from the two
technique’s embedding algorithms will be measured using Peak Signal to Noise
Ratio(PSNR). section 3.2 and section 3.3 will descuss the two stage’s embedding and
extracting algorithms in details.
3.2 Least Significant Bit Technique
The Least Significant Bit technique embedding algorithm embed the secret message into
the least significant bit of the image. The embedding process starts from the first
character to the end of the secret message. Each character of the secret message will
firstly be change into binary format according to the American Standard Code for
Information Interchange (ASCII) code, and then embed each bit of each character
starting from the least significant bit to the most significant bit (20
to 27
). 1 byte of

23
image data can only hide 1 bit of message. Thus, 8 bytes of image data will be needed to
hide 1 character because each character is made up of 8 bits. As for extracting a loop
function is used until the end of the secret message is found. The Least Significant Bit’s
embedding algorithm is shown in Figure 3.2 where Figure 3.3 shows the Least
Significant Bit’s extracting algorithm.
Figure 3.2: LSB embedding algorithm
Figure 3.3: LSB extracting algorithm
BMP Stego
ExtractingAlgorithm Secret Message
BMP Cover
BMP Cover
Secret Message
Embedding Algorithm BMP Stego

24
3.3 Discrete Cosine Transform Technique
The Discrete Cosine Transform (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. DCT is used in steganography as- Image is
broken into 8×8 blocks of pixels. Working from left to right, top to bottom, the DCT is
applied to each block. Each block is compressed through quantization table to scale the
DCT coefficients and message is embedded in DCT coefficients. Figure 3.4 shows the
main procedures for all encoding and message embedding processes based on the DCT
where Figure 3.5 shows the procedures for all decoding and message extracting
processes based on the DCT.
Figure 3.4: DCT based embedding algorithm

54
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