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Download the complete computer science project topic and material (chapter 1-5) titled An Improved Image Steganography Based On Least Significant Bit Matching Revisited (LSBMR) Using Sobel Edge Detection here on PROJECTS.ng. See below for the abstract, table of contents, list of figures, list of tables, list of appendices, list of abbreviations and chapter one. Click the DOWNLOAD NOW button to get the complete project work instantly.

 

PROJECT TOPIC AND MATERIAL ON An Improved Image Steganography Based On Least Significant Bit Matching Revisited (LSBMR) Using Sobel Edge Detection

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  • Name: An Improved Image Steganography Based On Least Significant Bit Matching Revisited
  • Type: PDF and MS Word (DOC)
  • Size: 1.64MB
  • Length: 88 Pages

 

ABSTRACT

Image steganography is the science of hiding data for securing confidential communication and it is the most popular type of carrier to hold information. Many algorithms have been proposed to hide information into digital images. The least significant bit algorithm (LSB) as one of the algorithms proposed is widely used in message embedding. However, the robustness of the algorithm based on LSB is low. The hidden message is usually destroyed when some image operations like resizing, cropping and rotation are applied to the stego-image. To overcome this limitation, this work proposed an improved image steganography based on least significant bit matching revisited (LSBMR) using Sobel edge detection that withstands image operations like resizing, rotation and cropping. The proposed method employs 2-dimensional discrete cosine transformation (2D-DCT) to transform the detected edges of the cover image pixel value into its coefficient, embeds the secret message in the coefficients of the detected edges of the cover image which was implemented in Netbeans IDE. Experimental results produced better stego-image quality that is robust against multiple image operations such as resizing and cropping. The statistical steganalysis tools such as Virtual Steganographic Laboratory (VSL) and StegExpose cannot detect the presence of secret information in the stego-image. Also, the proposed system generated stego-image with Peak Signal to Noise Ratio (PSNR) that is an image quality metric of 68 decibels (dB) for 8000 bits of secret message as regards to the invisibility over the existing steganography technique.

TABLE OF CONTENTS

ABLE OF CONTENTS
Title Page …………………………………………………………………………………………………………………….. iii
DECLARATION …………………………………………………………………………………………………………. iii
CERTIFICATION ……………………………………………………………………………………………………….. iv
DEDICATION ……………………………………………………………………………………………………………… v
ACKNOWLEDGEMENTS ………………………………………………………………………………………….. vi
TABLE OF CONTENTS ……………………………………………………………………………………………. viii
LIST OF TABLES ……………………………………………………………………………………………………….. xi
LIST OF FIGURES …………………………………………………………………………………………………….. xii
LIST OF APPENDICES …………………………………………………………………………………………….. xiv
ABBREVIATIONS, DEFINITIONS, GLOSSARIES AND SYMBOLS …………………………. xv
ABSTRACT ……………………………………………………………………………………………………………….. xvi
CHAPTER ONE: INTRODUCTION …………………………………………………………………………….. 1
1.1 Background to the Study ………………………………………………………………………………….. 1
1.2 Problem Statement …………………………………………………………………………………………… 5
1.3 Research Motivation ………………………………………………………………………………………… 6
1.4 Research Aim and Objectives …………………………………………………………………………… 6
1.5 Research Methodology ……………………………………………………………………………………… 7
1.6 Research Contributions to Knowledge………………………. Error! Bookmark not defined.
1.7 Organization of the rest of the Dissertation ……………………………………………………….. 8
1.8 Summary …………………………………………………………………. Error! Bookmark not defined.
CHAPTER TWO: LITERATURE REVIEW …………………………………………………………………. 9
2.1 Introduction …………………………………………………………….. Error! Bookmark not defined.
2.2 Information Security ……………………………………………………………………………………….. 9
2.3 Information Hiding ………………………………………………………………………………………… 10
ix
2.4 Cryptography ………………………………………………………………………………………………… 11
2.4.1 Features of Cryptography ……………………………………………………………………………….. 14
2.5 Steganography ……………………………………………………………………………………………….. 14
2.5.1 Features of Steganography ………………………………………………………………………………. 16
2.6 Classification/ Categorization of Steganography ……………………………………………… 19
2.6.1 Techniques Based on Algorithms …………………………………………………………………….. 19
2.6.2 Based on Cover Type ……………………………………………………………………………………… 20
2.7 Digital Images ………………………………………………………………………………………………… 21
2.8 Image Steganography …………………………………………………………………………………….. 22
2.8.1 Spatial Domain Based Image Steganography …………………………………………………….. 23
2.8.2 Transform (Frequency) Domain Based Image Steganography …………………………….. 27
2.9 Steganalysis ……………………………………………………………………………………………………. 32
2.10 Review of Related Work ……………………………………………………………………………… 33
2.11 Gap in Literature ………………………………………………………………………………………… 37
2.12 Summary ……………………………………………………………… Error! Bookmark not defined.
CHAPTER THREE: PROPOSED DESIGN ………………………………………………………………… 38
3.1 Introduction …………………………………………………………….. Error! Bookmark not defined.
3.2 Improved Image Steganography based on LSBMR using Sobel Edge Detection ………………………………………………………………………………………………………………….. 38
3.3 System Flow Diagram …………………………………………………………………………………….. 38
3.3.1 Proposed Algorithm for Message Hiding (Embedding) Process ….. …………………41
3.3.2 Proposed Algorithm for Message Extraction Process …………………………………………. 42
3.5 Summary ……………………………………………………………………………………………………….. 43
CHAPTER FOUR: IMPLEMENTATION, RESULTS AND ANALYSIS ……………………… 46
4.1 Introduction …………………………………………………………….. Error! Bookmark not defined.
4.2 System Requirement ………………………………………………… Error! Bookmark not defined.
4.3 Implementation Detail ……………………………………………………………………………………. 46
x
4.4 Graphical User Interface of the Proposed System ……………………………………………. 46
4.4.1 Message Hiding …………………………………………………………………………………………….. 47
4.4.2 Message Extraction ………………………………………………………………………………………… 48
4.5 Experimental Results and Analysis …………………………………………………………………. 49
4.5.1 Dataset …………………………………………………………………………………………………………. 49
4.5.2 Robustness ……………………………………………………………………………………………………. 50
4.5.3 Invisibility …………………………………………………………………………………………………….. 53
4.5.4 Undetectability ………………………………………………………………………………………………. 57
4.5.5 Security ………………………………………………………………………………………………………… 59
4.6 Summary …………………………………………………………………. Error! Bookmark not defined.
CHAPTER FIVE: SUMMARY, CONCLUSION AND FUTURE WORK …………………….. 60
5.1 Summary ……………………………………………………………………………………………………….. 60
5.2 Conclusion ……………………………………………………………………………………………………… 60
5.3 Future Work ………………………………………………………………………………………………….. 61
REFERENCES ……………………………………………………………………………………………………………. 62
APPENDIX 1: SAMPLE PROGRAM CODING ………………………………………………………….. 71
xi
LIST OF TABLES
Table 4.1: Average PSNR and MSE of 200 Stego-images………………………………………………….. 55
Table 4.2: Result of Five Samples Stego-images Generated from the Steganalysis Tools ………. 58
Table 4.3: Security Features of the Steganographic Techniques ………………………………………….. 59
xii
LIST OF FIGURES
Figure 1.1: Modern Steganography System ……………………………………………………………………….. 3
Figure 2.1: Taxonomy of Security System ……………………………………………………………………….. 11
Figure 2.2: Encryption Process ……………………………………………………………………………………….. 12
Figure 2.3: General Steganography Model ……………………………………………………………………….. 16
Figure 2.4: Steganography Triangle ………………………………………………………………………………… 17
Figure 2.5: An Image Deer and Pixels of Part of the Image ……………………………………………… 22
Figure 2.6: General Model of Image Steganography …………………………………………………………. 23
Figure 2.7: Spatial Domain Steganography Techniques …………………………………………………….. 24
Figure 2.8: Sobel Edge Masks ………………………………………………………………………………………… 27
Figure 2.9: Sample DCT Block ………………………………………………………………………………………. 30
Figure 2.10: (a) The Quantization Table (b) Quantized DCT (c) Dequantized DCT Block …….. 30
Figure 2.11: Zigzag Scanning Order ………………………………………………………………………………… 32
Figure 2.12: LSBMR Embedding Algorithm ……………………………………………………………………. 34
Figure 2.13: The Data Embedding and Extracting Process of Edge Adaptive based on LSBMR. ………………………………………………………………………………………………………………………. 35
Figure 2.14: Data Hiding Process and Data Extraction Process …………………………………………… 36
Figure 3.1: Flow Diagram of Proposed System (a) Message Hiding and (b) Message Extraction …………………………………………………………………………………………………………………….. 39
Figure 3.2: Combined DetailedBlock Diagram of the Proposed System ………………………………. 40
Figure 4.1: The GUI of Improved Image Steganography based on LSBMR Application ……….. 47
Figure 4.2: A Snapshot for Message Hiding ……………………………………………………………………… 48
Figure 4.3: A Snapshot for Message Extraction ………………………………………………………………… 49
xiii
Figure 4.4i: Proposed Techinque (a) Cropped Stego-image (b) Recovered Message from Cropped Image ……………………………………………………………………………………………………………… 51
Figure 4.4ii: Existing Techinque (a) Cropped Stego-image (b) Message Lost from Cropped Image ………………………………………………………………………………………………………………………….. 51
Figure 4.5i: Proposed Techinque (a) Resized Stego-image (b) Recovered Message from Resized Image ………………………………………………………………………………………………………………. 52
Figure 4.5ii: Existing Techinque (a) Resized Stego-image (b) Message Lost from Resized Image ………………………………………………………………………………………………………………………….. 52
Figure 4.6i: Proposed Techinque (a) Rotated Stego-image (b) Recovered Message from Rotated Image ………………………………………………………………………………………………………………. 53
Figure 4.6ii: Existing Techinque (a) Rotated Stego-image (b) Message Lost from Rotated Image ………………………………………………………………………………………………………………………….. 53
Figure 4.7: Bar Chart of Average PSNR using the Two Steganographic Techniques for Different Hiding Capacity ……………………………………………………………………………………………… 56
Figure 4.8: Modification Rate of the Two Steganographic Methods for Different Embedding Capacity ……………………………………………………………………………………………………… 57
LIST OF APPENDICES
Appendix 1: Sample Program Coding……………………………………………………..71
ABBREVIATIONS, DEFINITIONS, GLOSSARIES AND SYMBOLS
ACRONYM DEFINITION

2D-DCT 2-Dimensional Discrete Cosine Transform

BMP BitMaP BPP Bit Per Pixel

DCT Discrete Cosine Transform

EALSBMR Edge Adaptive Least Significant Bit Matching Revisited

GIF Graphical Interchange Format

HVS Human Visual System

IDCT Inverse Discrete Cosine Transform

IDE Integrated Development Environment

JPEG Joint Photographic Expert Group

LSB Least Significant Bit

LSBMR Least Significant Bit Matching Revisited

MSE Mean Squared Error Pixel (s) Picture Element (s)

PNG Portable Network Group

PSNR Peak Signal to Noise Ratio

PRNG Pseudo Random Number Generator

RGB Red Green and Blue

RS Regular Singular

VSL Virtual Steganographic Laboratory

CHAPTER ONE

INTRODUCTION
This chapter discusses the introductory part of this dissertation, which includes the background to the study, problem statements and research motivation, the research aim and objectives, research methodology, contribution to knowledge and finally the organization of the rest of the dissertation.

1.1 Background to the Study
Recently, people exchange information using the existing communication technologies such as the internet and huge volume of data transfer takes place via the plethora of services offered by the web. This information can be very sensitive and need to be protected against any attacker who tries to intercept them during the transmission stage. According to Ratnakirti et al., (2013), data over internet may be stolen, intercepted, illegally modified or even destroyed by an adversary resulting in intellectual property rights infringement, data loss, data leakage and data damage. Transmitting top secret information cannot be solely relied on the existing communication channels because the technologies are vulnerable to attacks Osama, (2005) and exchanged information can be detected relatively easily. Therefore, it is vital to protect the privacy and confidentiality of top secret message during its transit through the internet. To preserve the privacy and confidentiality of important data over the internet it must be provided with a metaphorical envelope such that its contents are revealed only to the intended receiver Ratnakirti et al., (2013) without arousing suspicions. Data hiding techniques such as steganography precisely aim at performing this task.
The steganography technique has been used many years ago to convey secret messages. For instance, Greek historian Herodotus was the first to document the usage of steganography to send messages (Aubrey, 1996). A slave was sent by his master to deliver a secret message tattooed on his scalp. After the message was tattooed, the slave waited until his hair grew back and concealed the message. The most popular steganographic methods between the 13th and 16th century involved written text. One method used a mask, a paper with holes, shared between the sender and recipient. The mask was simply put over the text and the message was revealed. Francis Bacon realized that two different fonts for each letter can be applied to embed binary representations of messages. Holub, (2014), stated that ―Brewster devised a very original technique in 1857, which was later used in several wars‖. Security of most of the previously mentioned methods was achieved only by assuming ignorance of the adversary. This is sometimes pejoratively called security through obscurity. The adversary did not attempt any targeted attack in the sense of modern steganalysis, instead they trained spies and secret services to obtain the necessary information by other means (Fridrich, 2009). Although steganography is an ancient subject, the modern formulation of it is often given in terms of the prisoner‘s problem proposed by Simmons (Patel and Gadhiya, 2015), where two inmates wish to communicate in secret to hatch an escape plan. All of their communication passes through a warden who will throw them in solitary confinement should he/she suspect any covert communication (Chandramouli et al., 2003).
The warden, who is free to examine all communication exchanged between the inmates, can either be passive or active. A passive warden simply examines the communication to try and determine if it potentially contains secret information. If she suspects a communication to contain hidden information, a passive warden takes note of the detected covert communication, reports this to some outside party and lets the message through without blocking it. An active warden, on the other hand, will try to alter the communication with the suspected hidden information deliberately, in order to remove the
information (Anderson and Petitcolas, 1998). Figure 1.1 represents modern steganography.
Generally steganography is known as ―invisible‖ communication of hiding secret messages
into digital cover-media such that attackers will not be aware of the existence of the hidden
messages (Micheal and Herbert, 2011). It is a mechanism that completely differs from
cryptography. In fact, in cryptography the information is modified but still can be seen in
this unreadable format once sent over the networks, whereas in steganography the
information is simply embedded into a digital support and cannot be noticed as long as the
quality of the carrier is not deteriorated (Zohreh and Jihad, 2014).
Steganography hides information in a variety of multimedia carriers that include video clip,
a digital image, an audio file or text called cover object. Once the information is embedded
in any of the cover media it is called stego-object. If the cover is an image or video file,
then the result of embedding the information in the cover is referred to as stego-image or
stego-video respectively.
Figure 1.1: Modern Steganography System (Por et al., 2008)

It is shown that images are excellent carriers to hide and exchange sensible information
over networks (Rodrigues et al., 2004). Many algorithms have been proposed recently to
hide information into images and preserve their quality. In this dissertation, we focus on image steganography algorithms because image used as a host object was observed to have low communication cost and availability of large number of redundant bits. An image consists of light luminance or pixels represented as an array of values at different points. A pixel consists of one byte or more. For example in 8-bit images each pixel consists of 1 byte (i.e., 8 bits). While each pixel in a 24-bit image is represented as three bytes representing the Red, Green and Blue (RGB) colors (Caldwell, 2003).
Image steganography has many applications, especially in today‘s modern, high-tech world. Most people on the internet have a concern about privacy and secrecy. For two parties, image steganography allows to communicate secretly (Kamred, 2014). For some morally-conscious people is allowed to safely whistle blow on internal actions (Sahar, 2015). Also, it allows for copyright protection on digital files using the message as a digital watermark. One of the other main applications for image steganography is for the high-level or top-secret documents transportation between international governments (Phad et al., 2012). In medicine, medical practitioners can embed some information such as name, comments or diagnosis of the patient into their medical imagery and exams. Then medical images can be of different types such as embedding information into ECG images (Ibaida et al., 2010). In military, not only the content of the communication but also the communication itself between agencies must be kept secret. Information hiding technique can be used when two or more agencies communicate via digital short radio (Jiang et al., 2009). In smart id‘s, information of the person is embedded inside their image for confidential information (Flores‐Escalante et al., 2012). In remote sensing, information can be hidden into some site images to provide secret only to authorized users (Wang and Niu, 2008). In e-commerce, registration information can be hidden in electronic papers that can be used to identify authentication based on steganographic techniques (Wang and Ye, 2010). Also, it can be used in several areas such as: printers, database systems, human rights organization and correcting media transition errors. There exist several embedding algorithms for image steganography both in spatial and transform domains. But most algorithms in the spatial domain are vulnerable to image processing operations such as cropping, resizing and rotation. These problems, however, are of utmost importance; therefore there is need for continued improvement.

1.2 Problem Statement
Many research works have been conducted on spatial domain image steganography-based algorithms. Least significant bit (LSB) replacement in the spatial domain is a well-known steganographic method. In this embedding scheme, only the LSB plane of the cover image is directly overwritten with the secret bit stream according to a pseudorandom number generator (PRNG). Subhedar and Mankar, (2014) stated that ―spatial domain steganography schemes achieve high embedding capacities, but they are vulnerable to small modifications that may result from image processing operations such as cropping, rotation, scaling and resizing‖. It was investigated that the robustness of the image steganography algorithm based on LSBMR using Sobel edge detection is very low. The hidden message was destroyed when some image operations such as cropping, resizing and rotation were applied to the stego-image. Hence, the need to design and implement an improved image steganography based on LSBMR using Sobel Edge Detection that is robust against any image operations and works in transform domain.

1.3 Research Motivation
Security of information during transmission is a major issue in this modern era. All of the communicating bodies want confidentiality, integrity, and authenticity of their secret information. Since an unhidden coded message, no matter how unbreakable it is, will arouse suspicion. In addition, due to the problems and challenges posed by the existing image steganographic algorithm, which is based on LSB in spatial domain specifically the work of (Zohreh and Jihad, 2014). Therefore, the need arises for an improved image steganography based on LSBMR using Sobel Edge Detection that is robust enough to withstand any image operations (like cropping, resizing and rotation) and operates in transform domain.

1.4 Research Aim and Objectives
The aim of this research is to develop an improved image steganography based on Least Significant Bit Matching Revisited (LSBMR) using sobel edge detection that is robust against image operations like rotation, resizing and cropping. The objectives are to:
a. carry out process analysis on image steganography techniques;
b. design a modified image steganography based on LSBMR using Sobel Edge Detection;
c. employ 2D-DCT transformation to the detected edges of the cover image;
d. implement the proposed system;
e. evaluate and compare the performance of the technique side by side the work of (Zohreh and Jihad, 2014) in relation to robustness, undetectability and invisibility.

1.5 Research Methodology
The following steps were taken in the course of this research to achieve the stated objectives.
a. Review of related literature on security, encryption, steganography and digital images was conducted with a view to get ideas on how to enhance the work of (Zohreh and Jihad, 2014).
b. Modify the image steganography based on LSBMR using Sobel edge detection to make it more robust and work in transform domain.
c. Enhance the technique by employing 2D-DCT transformation technique so as to embed the secret message into the coefficients of the cover image rather than into the image pixels directly.
d. Application of standard matrix to quantize the coefficients before embedding the secret bits into the resultant coefficients.
e. Implement the enhanced algorithm using open source technologies such as Java Programming Language and Netbeans Integrated Development Environment (IDE).
f. Evaluate and compare the proposed system with the work of (Zohreh and Jihad, 2014) in relation to robustness, undetectability and invisibility using standard statistical steganalysis and image quality metrics such as PSNR and MSE steganalysis.

1.6 Organization of the Dissertation
The dissertation is structured as follows:

Chapter One: The general introduction has been presented in this chapter.

Chapter Two: This chapter reviews the relevant literature on information security. An overview of the information hiding techniques using steganography is stated in details. Categories of steganography algorithms are described. An overview of digital images used in steganography is presented. State-of-art image algorithms are also presented in this chapter.

Chapter Three: The methodology of improved image steganography based on LSBMR using Sobel edge detection on images is demonstrated. The message embedding and extraction processes of the proposed algorithm are presented in detail.

Chapter Four: The research implementation strategy of the system integrating the different steganography techniques is presented in this chapter. We also present analysis of the experimental results obtained to evaluate the performance of the method. Comparative performance results between the existing and the enhanced systems are presented.

Chapter Five: This chapter presented the summary of the research, the conclusion and future work.

 

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