Performance Analysis of LSB, MSB and Combined LSB-MSB Algorithm Interms of Image Quality and Encoding time.
TABLE OF CONTENTS
TABLE OF CONTENTS vi
LIST OF TABLES xi
CHAPTER ONE 1
1.1 BACKGROUND OF THE STUDY 1
1.2 STATEMENT OF THE PROBLEM 2
1.3 MOTIVATION OF THE STUDY 2
1.4 AIMS AND OBJECTIVES OF THE STUDY 2
1.5 OUTLINE OF METHODOLOGY 3
1.6 SCOPE OF THE STUDY 3
1.7 SIGNIFICANCE OF THE STUDY 4
1.8 OPERATIONAL DEFINITION OF TERMS 4
1.9 ORGANISATION OF THE PROJECT 6
CHAPTER TWO 7
REVIEW OF LITERATURE 7
2.1 HISTORY OF DATA COMPRESSION 7
2.2 RESEARCH WORK ON STEGANOGRAPHY 8
CHAPTER THREE 11
3.1.1 FACTS FINDING 11
3.1.2 ANALYSIS OF THE EXISTING SYSTEM(S) 12
3.1.3 ANALYISIS PROCEDURE 19
CHAPTER FOUR 21
RESEARCH RESULT 21
4.1 INTRODUCTION 21
4.2 PROGRAMMING LANGUAGE SELECTION 21
4.3 RESULT OF EXPERIMENTATION 21
CHAPTER FIVE 36
CONCLUSION, SUMMARY AND RECOMMENDATION 36
5.1 SUMMARY 36
5.2 CONCLUSION 37
5.3 RECOMMENDATION 37
LIST OF FIGURES
Figure 1.1 An illustration of a simple steganographic process 4
Figure 1.2 Illustration of the least significant bit 5
Figure 1.3 Illustration of the most significant bit 6
Figure 3.1 Typical Steganographic System 12
Figure 3.2 Combined Algorithm Flowchart 16
Fig 4.1a Original image of a rose 22
Fig 4.1b LSB Algorithm on the rose image 23
Fig 4.1c MSB algorithm on the rose image 23
Fig 4.1d Combined LSB – MSB algorithm applied on the rose 24
Fig 4.2a Original image of a giraffe image 24
Fig 4.2b LSB Algorithm on the giraffe image 25
Fig 4.2c MSB algorithm on the giraffe image 25
Fig 4.2d Combined LSB – MSB algorithm applied on the giraffe 26
Fig 4.1a Original image of a pepper 26
Fig 4.1b LSB Algorithm on the pepper image 27
Fig 4.2c MSB algorithm on the pepper image 27
Fig 4.1d Combined LSB – MSB algorithm applied on the pepper image 28
Fig 4.1a Original image of a pepper 28
Fig 4.1b LSB Algorithm on the pepper image 29
Fig 4.2c MSB algorithm on the pepper image 29
Fig 4.1d Combined LSB – MSB algorithm applied on the pepper image 30
Fig 4.5 Interface of the Software 31
Fig 4.6 Selecting the image to conduct stenography 32
Fig 4.7 The pop up that confirms that the stego-image has been extracted 33
LIST OF TABLES
Table 4.1 Results of the Experimentation 35
1.1 BACKGROUND OF THE STUDY
The background of this project work is based on the concept of data compression and encoding. This data compression and encoding is often found in fields such as steganography, video editing and so on. For this project work, we will be focusing on steganography to test each of the three algorithms on image quality and encoding time. There are certain algorithms that these actions which are carried out use to achieve the end result. The algorithms that close attention will be paid to are the LSB(Least Significant Bit), the MSB(Most Significant Bit) and the combined LSB – MSB algorithms.
The Least Significant Bit Algorithm is known for its “security through obscurity” which is hiding data within a cover image or audio in its least significant bit. The LSB is the lowest bit in a series of numbers in a binary system. The LSB is often used to hide text or an image within a cover image. It operates on the principle that the human eye cannot distinguish between two shades separated by a bit.
The Most Significant Bit Algorithm is based on the Most Significant Bit or the highest bit value in a series of numbers in a binary system. In this algorithm, a secret message is embedded in the most significant bit of the pixel of an image.
The Combined LSB – MSB algorithm is an hybrid combination of the aforementioned algorithms. It is also referred to by some as New Hybrid(the combined LSB – HSB algorithm). Wai et al(2018). It works by combining the LSB and the MSB techniques into an hybrid algorithm that embeds the secret message bits into the least significant bit of and the most significant bit of the cover image.
1.2 STATEMENT OF THE PROBLEM
There is an age long (at least since the electronic era) problem of finding the most efficient algorithms to encode and compress data formats. However, there are limitations in certain areas in certain algorithms which affect performance in terms of image quality and encoding time. This study aims to test optimally, through series of analysis algorithms on data formats which in this case will be images.
1.3 MOTIVATION OF THE STUDY
This study was motivated by the fact that we can use multiple different methods to perform information/text hiding and encoding in steganography. We want to see which algorithm(s) can be used in particular situations.
1.4 AIMS AND OBJECTIVES OF THE STUDY
The purpose of the Performance analysis of LSB, MSB and the combined LSB – MSB algorithm is to:
- Analyse the performance of the aforementioned algorithms on image and image production and compression.
- Discover optimal algorithms to use when performing compression and encoding functions.
- Improve constantly each algorithm and model.
- Combine the MSB and LSB techniques into an hybrid algorithm that embeds secret message bits into the least significant bit and most significant bit of the cover image.
- Compare the three algorithms in terms of encoding time
- Test the algorithms using different image formats and observe the quality of image.
1.5 OUTLINE OF METHODOLOGY
The implementation of this idea and project was done with the Java programming language. This was chosen because of its cross-platform nature and its general use for steganography and cryptography of a wide range of image formats.
1.6 SCOPE OF THE STUDY
The study covers encoding of a number of picture formats like .jpeg, .gif and .bmp using three algorithms and analyzing their performance on results. We also examine challenges and techniques to address them. For this project we would examine on a smaller scale and test it before then broadening the scope.
1.7 SIGNIFICANCE OF THE STUDY
The study is very significant in studying, analyzing and postulating more efficient and trustworthy ways of encoding and compressing images. It also explores the aspects where the LSB based, MSB based and LSB – MSB based steganographic techniques are applied and can be improved.