Neighbour Pixel Color Correlativity On Image Steganalysis

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Faculty of Information Technology and Communication

NEIGHBOUR PIXEL COLOR CORRELATIVITY ON IMAGE

STEGANALYSIS

Alaa Abdulhussein Jabbar

Doctor of Philosophy

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NEIGHBOUR PIXEL COLOR CORRELATIVITY ON IMAGE

STEGANALYSIS

ALAA ABDULHUSSEIN JABBAR

A thesis submitted

in fulfillment of the requirements for the degree of Doctor of Philosophy

Faculty of Information Technology and Communication

UNIVERSITI TEKNIKAL MALAYSIA MELAKA

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DECLARATION

I declare that this thesis entitled “Neighbour Pixel Color Correlativity on Image Steganalysis” is the result of my own research except as cited in the references. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.

Signature :

Name : Alaa Abdulhussein Jabbar

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0B

APPROVAL

I hereby declare that I have read this thesis and in my opinion, this thesis is sufficient in terms of scope and quality for the award of Doctor of Philosophy.

Signature :

Supervisor Name : SHAHRIN BIN SAHIB @ SAHIBUDDIN Date :

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DEDICATION

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ABSTRACT

Steganalysis is science of discovering presence of secretively embedded data within the potential carriers. Since images are one of the most commonly used digital media on networks, they grab less attention than other digital media types. As a result, images are very appropriate cover for concealing presence of secret data transmission. Insertion of external data degrades the natural color correlativity among the pixels. Statistical steganalysis is an effective approach for discovering the generated anamolies through embedding process in content of images. Various statistical steganalysis techniques have been designed to discover presence of embedding artifacts and anomalies. However, the current practices are not efficient enough for detection of steganography. To overcome the problem, this research proposes a novel statistical steganalysis technique which efficienctly detects data embedding. Since this method statistically examines the suspicious carriers then it has more capability in distinguishing effects of new steganographic techniques. Color correlativity among image pixels varies based on image content and data embedding degrades this natural correlativity. This research classifies images in four image themes and then studies their behaviors after data embedding in different embedding ratios. The proposed method examines color correlativity of pixels of the given image with the neighbor pixels and then compares the results with the extracted color correlativity behavioral patterns. The most similar color correlativity behavioral pattern determines image theme of the analyzed image and its embedding ratio. Efficiency evaluation of the designed color correlativity statistical steganalysis method shows outstanding efficiency ehancement in comparison with the current practices. The designed technique efficiently determines both image theme and embedding ratio. The efficiency dramatically increases in detecting either of image theme or estimating message length.

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ABSTRAK

Steganalisis adalah suatu bidang sains dalam mengenal pasti data rahsia yang tersembunyi disebalik media tersurat. Memandangkan imej adalah digital media biasa digunakan dalam talian rangkaian, ianya mudah terlepas pandang berbanding media lain. Ini menjadikan imej sebagai media yang sesuai untuk menyembunyikan kehadiran saluran data rahsia. Kemasukan data luar secara amnya akan menjejaskan keaslian corak hubung-kait warna antara piksel imej. Steganalysis statistik merupakan kaedah yang berkesan bagi mengesan kehadiran data asing yang terhasil semasa proses penyembunyian data rahsia dalam isi kandungan imej. Pelbagai teknik steganalysis statistik telah dicipta bagi mengesan kehadiran data tersembunyi dan barangan asing dalam sesuatu imej. Namun, amalan masa kini masih lagi kurang efisien bagi mengesan data rahsia stegano. Bagi mengatasi masalah ini, penyelidikan ini mencadangkan teknik steganalysis statistik baru yang lebih berkesan dalam mengesan data rahsia stegano tersembunyi. Memandangkan kaedah ini memeriksa imej media pembawa yang diragui, maka ianya lebih berkeupayaan dalam mengenalpasti kesan biasan teknik stegano baru. Kesalinghubungan antara warna piksel imej adalah pelbagai bergantung kepada isi kandungan imej dan penyembunyian data merungkaikan lagi keaslian hubungkait antara warna piksel. Penyelidikan ini akan menkelaskan imej kepada empat(4) tema dan membuat kajian tentang kesan sampingan dari kehadiran data rahsia pada nisbah yang berbeza. Kaedah ini akan memeriksa hubungkait antara warna piksel dengan piksel berjiranan dan dibandingkan dengan hubungkait antara corak warna piksel asli. Corak hubungkait antara warna piksel yang terdekat akan menentukan tema imej dan nisbah penyembunyian yang sesuai. Penilaian keberkesanan kaedah steganalisis hubungkait statistik antara warna piksel yang direka akan menunjukkan peningkatan berbanding amalan semasa. Kaedah baru yang diperkenalkan dan dianjurkan akan menentukan tema imej dan nisbah penyembunyian data. Peningkatan keberkesanan dapat dilihat dalam mengenalpasti tema imej atau menjangka berapa panjangnya mesej.

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ACKNOWLEDGEMENTS

First and foremost, I would like to take this opportunity to express my sincere acknowledgement to my supervisor Professor Dr. Shahrin Bin Sahib @ Sahibuddin Dean of ICT faculty of Universiti Teknikal Malaysia Melaka (UTeM) for his essential supervision, support and encouragement towards the completion of this thesis.

I would also like to express my greatest gratitude to Senior Lecturer Dr. Mazdak Zamani Senior Lecturer in Advanced Informatics School of Universiti of Teknology Malaysia (UTM AIS) , co-supervisor of this project for his advice and suggestions. Special thanks to all my peers, beloved father, mother, wife, and siblings for their moral support in completing this degree. Lastly thank you to everyone who has been to the crucial parts of realization of this project.

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iv TABLE OF CONTENTS PAGE DECLARATION APROVAL DEDICATION ABSTRACT i ABSTRAK ii ACKNOWLEDGEMENT iii TABLE OF CONTENTS iv

LIST OF TABLES viii

LIST OF FIGURES xiv

LIST OF ABBREVIATIONS xix

LIST OF APPENDICES xx

CHAPTER

1. INTRODUCTION 1

1.0 Data Hiding Background

1.1 Problem background 1 4 1.2 Problem statement 5 1.3 Research questions 6 1.4 Research objectives 8 1.5 Terminologu justification 8 1.6 Scope of study 9 1.7 Significance of study 10 1.8 Research contribution 1.9 Thesis organization 11 12 1.10 Summary 14 2. LITERATURE REVIEW 15

2.1 History of steganography and watermarking 16

2.2 Steganography and watermarking definitions in CIA triangle 18

2.2.1 Compression ratio 18

2.2.2 Multiple watermarks 18

2.2.3 Success rate and embedding complexity 18

2.2.4 Detection complexity 18 2.2.5 Secret key 19 2.2.6 Statistical invisibility 19 2.2.7 Secrecy 20 2.2.8 Computational cost 21 2.2.9 Redundancy 21

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2.3 Important stegaography measurements 23

2.3.1 Capacity 23 2.3.2 Robustness 24 2.3.3 Imperceptibility 24 2.3.4 Security 25 2.4 Steganography types 28 2.4.1 Fragile Steganography 30 2.4.2 Robust steganography 31 2.5 Steganography techniques 32 2.5.1 Binary-file technique 32

2.5.2 Text steganography techniques 34

2.5.2.1 Line shift coding protocol 34 2.5.2.2 Word Shift coding protocol 35 2.5.2.3 Feature coding protocol 35 2.5.2.4 White spectrum manipulation 36

2.5.2.5 Text content 36

2.5.3 XML 36

2.5.4 Image Steganography techniques 38

2.5.4.1 Least Significant Bit (LSB) 38 2.5.4.2 Direct Cosine Transformation 40 2.5.4.3 Wavelet Transformation 42

2.5.5 Audio data hiding 45

2.5.5.1 Spread spectrum 45 2.5.5.2 MIDI 45 2.5.5.3 MP3 46 2.5.6 Video 48 2.5.7 DNA 49 2.5.8 Hybrid steganography 49

2.6 Steganalysis and steganalysis attacks 51

2.7 Cryptography 53

2.8 Review of the related works 56

2.8.1 Review of the related steganography and watermarking

algorithm 56

2.8.2 Review of the integration of steganography and

cryptography algorithms 57

2.8.3 Review of the related image steganalysis algorithms 58

2.9 Introduction to research methodology 64

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3. RESEARCH METHODOLOGY 68

3.1 Research design 68

3.1.1 Objective 1: An exploralary study on pixel correlativity 68 3.1.2 Objective 2: An extraction on pixel correlativity behavior 69 3.1.3 Objective 3: Image them detaction according to pixel correlativity 69 3.1.4 Objective 4: Evaluation of the steganalysis method 69

3.2 Research procedures and activities 70

3.2.1 Determining optimized analysis border 70 3.2.2 Extracting color correlativity behavioral pattern 71 3.2.3 Scoring similarity of color correlativity analysis results with

color correlativity behavioral patterns 72 3.2.4 Tool development for color correlativity analysis and

steganalysis 74

3.2.5 Sampling and efficiency evaluation 74

3.3 Summary 77

4. COMPOUND NEIGHBOR PIXEL COLOR CORRELATIVITY

STATISTICAL STEGANALYSIS METHOD 78

4.1 Color correlativity analysis of pixels 78

4.1.1 Pixel color (AND) correlativity analysis 81 4.1.2 Channel color (OR) correlativity analysis 85

4.1.3 Color correlativity patterns 89

4.1.4 Purifying color correlativity analysis results by standard deviation for generating color correlativity behavioral patterns

89

4.1.5 Source of dataset images 92

4.1.6 Determining layer examination threshold 93

4.1.7 Pattern matching system 94

4.2 Image themes 98

4.2.1 Wide Flat Region 99

4.2.2 Wide Smooth Region 104

4.2.3 Partial smooth region images 108

4.2.4 Edgy images 112

4.3 Determining efficient boundary of neighbor layer analysis 118

4.3.1 Optimized layer for AND color correlativity steganalysis 121 4.3.2 Optimized layer for OR color correlativity steganalysis 125

4.4 Summary 129

5. STATISTICAL ANALYSIS AND COLOR CORRELATIVITY

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5.1 Statistics and extracted color correlativity behavioral patterns of

primary dataset original images 131

embedded wide flat region images 135

embedded wide smooth region images 146

embedded partial smooth region images 157

embedded edgy images 168

5.6 Summary 179

6. EVALUATION OF THE COMPOUND NEIGHBOR PIXEL COLOR CORRELATIVITY STATISTICAL STEGANALYSIS METHOD

180

6.1 Primary dataset steganalysis results 181

6.1.1 Theme-based steganalysis results 182

6.1.2 Blind steganalysis results

6.1.3 Steganalysis performance evaluation on edgy images of primary dataset

184 187

6.2 Secondary dataset steganalysis results 187

6.2.1 Theme-based steganalysis results 188

6.2.2 Blind steganalysis results

6.2.3 Steganalysis performance evaluation on edgy images of secondary dataset

189 190

6.3 Embedding ratio determination 191

6.3.1 Embedding ratio determination accuracy of Theme-based

steganalysis 192

6.3.2 Embedding ratio determination accuracy of blind

steganalysis 195

6.4 Image theme detection 200

6.5 Efficiency comparison of color correlativity statistical steganalysis algorithm and related image steganalysis algorithms of literature review

203

6.6 Summary 205

7. CONTRIBUTION AND CONCLUSION 206

7.1 Research objectives 206 7.2 Conclusion 207 7.3 Contributions 210 7.4 Future works 211 7.5 Summary 213 REFERENCES 214 APPENDICES 224

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LIST OF TABLES

TABLE TITLE PAGE

2.1: Comparison of steganography, watermarking and cryptographic properties 22

2.2: Sample of fragile watermarking algorithm for content authenticating (Alomari, R., and Al-Jaber 2004) 30

2.3: Sample of image LSB steganography algorithm 40

2.4: Drawbacks of spatial domain and DCT base domain steganography methods (Cheddad, A., Condell, A. J., Curran, K., Kevitt 2010) 41

2.5: Performance of typical image steganographic methods 43

2.6: Evaluation of the Image Steganography techniques 44

2.7: Comparison of image steganography algorithms 45

2.8: Summary and evaluation of Audio steganography Methods 47

2.9: Comparison of various steganography techniques 48

2.10: Hybrid steganography algorithm 50

2.11: Hybrid extraction and detection steganography algorithm 51

2.12: Steganalysis attack types 52

3.1: Structure of correlativity behavioral pattern for layer I and analysis value of X 72

4.3: Structure of correlativity behavioral pattern for 3 neighbor layers 92

4.4: Color correlativity steganalysis results of a sample image 96

4.5: Color correlativity behavioral pattern of 4-bits embedded wide flat region images 96

4.6: Similarity evaluation scoring details based on tables 4.4 and 4.5 values 97

4.7: AND and OR pixels’ color correlativity analysis of wide flat region images in 5 layers 102

4.8: AND and OR pixels’ color correlativity analysis of wide smooth region images in 5 layers 106

4.9: AND and OR pixels’ color correlativity analysis of partial smooth region images in 5 layers 110

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4.10:AND and OR pixels’ color correlativity analysis of edgy images in 5 layer 114 4.11: Average values of AND pixels’ color correlativity analysis of original copies of the four image themes from one to fifth layer 117 4.12: Average values of OR pixels’ color correlativity analysis of original copies of the four image themes from one to fifth layer 117 4.13: Number of pixels in each neighbor layer and load of required process for

each layer 119 4.14: Original and projection values of AND color correlativity analysis of original

images 121 4.15: Original and projection distance values between layers for AND correlativity

analysis 123 4.16: Average distance and process percentage between AND analysis values of

every two neighbour layers 123 4.17: Percent of correlated and unrelated AND analysis of pixels for each layer 125 4.18: Original and projection values of OR color correlativity analysis of original

images 126 4.19: Original and projection distance values between layers for OR correlativity

analysis 127 4.20: Average distance and process percentage between OR analysis values of every

two neighbour layers 127 4.21: Percent of correlated and unrelated OR analysis of pixels for each layer 129 5.1: Statistical analysis of color correlativity analysis of primary dataset original

wide flat region images 132 5.2: Extracted color correlativity behavioral pattern of original wide flat region

images 132 5.3: Statistical analysis of color correlativity analysis of primary dataset original wide smooth region images 133 5.4: Extracted color correlativity behavioral pattern of original wide smooth region

images 133 5.5: Statistical analysis of color correlativity analysis of primary dataset original

partial smooth region images 134 5.6: Extracted color correlativity behavioral pattern of original partial smooth

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5.7: Statistical analysis of color correlativity analysis of primary dataset original edgy images 135 5.8: Extracted color correlativity behavioral pattern of original edgy images 135 5.9: Statistical analysis of color correlativity analysis of primary dataset 1-bit

embedded wide flat region images 137 5.10: Extracted color correlativity behavioral pattern of 1-bit embedded wide flat

region images 138 5.11: Statistical analysis of color correlativity analysis of primary dataset 2-bits

embedded wide flat region images 139 5.12: Extracted color correlativity behavioral pattern of 2-bits embedded wide flat

region images 146 5.19: Statistical analysis of color correlativity analysis of primary dataset 1-bit

embedded wide smooth region images 148 5.20: Extracted color correlativity behavioral pattern of 1-bit embedded wide

smooth region images 149 5.21: Statistical analysis of color correlativity analysis of primary dataset 2-bits

embedded wide smooth region images 151 5.22: Extracted color correlativity behavioral pattern of 2-bits embedded wide

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5.24: Extracted color correlativity behavioral pattern of 3-bits embedded wide smooth region images 153 5.25: Statistical analysis of color correlativity analysis of primary dataset 4-bits

smooth region images 157 5.29: Statistical analysis of color correlativity analysis of primary dataset 1-bit

embedded partial smooth region images 159 5.30: Extracted color correlativity behavioral pattern of 1-bit embedded partial

embedded partial smooth region images 161 5.32: Extracted color correlativity behavioral pattern of 2-bits embedded partial

smooth region images 168 5.39: Statistical analysis of color correlativity analysis of primary dataset 1-bit

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5.40: Extracted color correlativity behavioral pattern of 1-bit embedded edgy region images 171 5. 41: Extracted color correlativity behavioral pattern of 1-bits embedded edgy

region images 173 5.42: Extracted color correlativity behavioral pattern of 2-bits embedded edgy

embedded edgy region images 175 5.44: Extracted color correlativity behavioral pattern of 3-bits embedded edgy

region images 179 6.1: Neighbor pixel color correlativity analysis values of a sample wide flat

region image 183 6.2: Matching score values calculated based on table 6.1 neighbor pixel color

correlativity values and wide flat region color correlativity behavioral patterns 183 6.3: Descending sorted order of calculated wide flat region images matching

scores 183 6.4: Meaning of the blind steganalysis identifier values 185 6.5: Neighbor pixel color correlativity analysis values of a sample given image

for blind analysis 185 6.6: Descending sorted order of match color correlativity behavioral patterns

based on color correlativity analysis results of table 6.5 186 6.A Theme-based steganalysis performance on edgy images of primary image

database 187 6.B Blind steganalysis performance on edgy images of primary image database 187

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6.C Theme-based steganalysis performance on edgy images of secondary image database 190 6.D Blind steganalysis performance on edgy images of secondary image database190 6.7: Embedding ratio determination accuracy of theme-based steganalysis of

primary dataset images 192 6.8: Embedding ratio determination accuracy of theme-based steganalysis of

secondary dataset images 193 6.9: Difference of accuracy of embedding ratio determination of theme-based

steganalysis between primary and secondary datasets 193 6.10: Embedding ratio determination accuracy of blind steganalysis of primary

dataset images 196 6.11: Embedding ratio determination accuracy of blind steganalysis of secondary

dataset images 197 6.12: Difference of accuracy of embedding ratio determination of blind

steganalysis between primary and secondary datasets 198 6.13: Image theme detection accuracy of compound color correlativity

steganalysis of primary dataset images 201 6.14: Image theme detection accuracy of compound color correlativity

steganalysis of secondary dataset images 201 6.15: Difference of accuracy of image theme detection of compound color

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LIST OF FIGURES

FIGURE TITLE PAGE

2.3: Classification of data hiding techniques 29

2.4: Steganography data embedding diagram 32

2.5: Steganography data extraction diagram 32

2.6: Binary file steganography 33

2.7: Process of embedding information in a document 34

2.8: Example of word shifting steganography technique 35

2.9: Illustration of first method of using XML tags for data hiding 37

2.10: Illustration of second method of using XML tags for data hiding 37

2.11: Illustration of element arrangement steganography 37

2.12: LSB steganography 38

2.13: Result of 1 bit (left) and 4 bits (right) embedding in a cover image 39

2.15: Hash function 55

2.16: Digital signing of a document 55

2.13: Histogram coefficient translation difference efficiency evaluation results 60

2.14: Support vector regression steganalysis efficiency evaluation results 61

2.15: Rate distortion curve steganalysis efficiency evaluation results 61

2.16: Efficiency evaluation results of neighborhood information steganalysis of 1-bit and 2-bits stego images 62

2.17: Efficiency evaluation results of adjacency pixel bits structure steganalysis 63

2.18: Efficiency evaluation results of difference distributions statistical modeling steganalysis 63

2.18: Illustration of neighbor layer meaning 65

3.1: Scoring method illustration in comparison of percentage of color correlativity of each analyzed layer with relevant color correlativity behavioral pattern 73

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3.3: Steps and procedures of running research 76

4.1: Illustration of Neighbor Layer Concept 80

4.2: Pixel analysis area 81

4.3: AND pixel correlativity analysis illustration 84

4.4: OR pixel correlativity analysis illustration 88

4.5: A plot of bell shaped distribution of data from mean point where each band has width of one standard deviation 90

4.6: Illustration of color correlativity comparison scoring system 95

4.7: Sample of images with big flat regions 100

4.8: Illustration of analysis of AND color correlativity in original wide flat region images 103

4.9: Illustration of analysis of OR color correlativity in original wide flat region images 104

4.10: Sample of images with wide smooth regions 105

4.11: Illustration of analysis of AND color correlativity in original wide smooth region images 107

4.12: Illustration of analysis of OR color correlativity in original wide smooth region images 108

4.13: Sample of images with partial smooth regions 109

4.14: Illustration of analysis of AND color correlativity in original partial smooth region images 111

4.15: Illustration of analysis of OR color correlativity in original partial smooth region images 112

4.16: Sample of edgy images 113

4.17: Illustration of analysis of AND color correlativity in edgy images 115

4.18: Illustration of analysis of OR color correlativity in original edgy images 116

4.21: AND pixel color correlativity analysis of original images of primary dataset in five layers 119

4.22: OR pixel color correlativity analysis of original images of primary dataset in five layers 120

4.23: Projection of AND correlativity analysis tracks 122

4.24: AND-analysis distance and analysis percentage among the layers 123

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4.26: OR-analysis distance and analysis percentage among the layers 127 5.1: Scattering depiction of AND color correlativity analysis of 1-bit embedded

primary dataset wide flat region images 136 5.2: Scattering depiction of OR color correlativity analysis of 1-bit embedded

primary dataset wide flat region images 137 5.3: Scattering depiction of AND color correlativity analysis of 2-bits embedded

primary dataset wide flat region images 138 5.4: Scattering depiction of OR color correlativity analysis of 2-bits embedded

primary dataset wide flat region images 145 5.11: Scattering depiction of AND color correlativity analysis of 1-bit embedded

primary dataset wide smooth region images 147 5.12: Scattering depiction of OR color correlativity analysis of 1-bit embedded

primary dataset wide smooth region images 148 5.13: Scattering depiction of AND color correlativity analysis of 2-bits embedded

primary dataset wide smooth region images 150 5.14: Scattering depiction of OR color correlativity analysis of 2-bits embedded

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5.17: Scattering depiction of AND color correlativity analysis of 4-bits embedded primary dataset wide smooth region images 154 5.18: Scattering depiction of OR color correlativity analysis of 4-bits embedded

primary dataset wide smooth region images 156 5.21: Scattering depiction of AND color correlativity analysis of 1-bit embedded

primary dataset partial smooth region images 158 5.22: Scattering depiction of OR color correlativity analysis of 1-bit embedded

primary dataset partial smooth region images 159 5.23: Scattering depiction of AND color correlativity analysis of 2-bits embedded

primary dataset partial smooth region images 160 5.24: Scattering depiction of OR color correlativity analysis of 2-bits embedded

primary dataset partial smooth region images 167 5.31: Scattering depiction of AND color correlativity analysis of 1-bit embedded

primary dataset edgy images 169 5.32: Scattering depiction of OR color correlativity analysis of 1-bit embedded

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5.33: Scattering depiction of AND color correlativity analysis of 2-bits embedded primary dataset edgy images 172 5.34: Scattering depiction of OR color correlativity analysis of 2-bits embedded

primary dataset edgy images 172 5.36: Scattering depiction of AND color correlativity analysis of 3-bits embedded

primary dataset edgy images 176 5.38: Scattering depiction of OR color correlativity analysis of 4-bits embedded

primary dataset edgy images 178 5.40: Scattering depiction of OR color correlativity analysis of 5-bits embedded

primary dataset edgy images 178 6.1: Illustration of accuracy of embedding ratio determination of theme-based

steganalysis on primary and secondary datasets 194 6.2: Illustration of accuracy of embedding ratio determination of theme-based

steganalysis with 1 bit accepted tolerance on primary and secondary datasets195 6.3: Illustration of accuracy of photo theme and embedding ratio determination

of blind steganalysis on primary and secondary datasets 198 6.4: Illustration of accuracy of embedding ratio determination of blind

steganalysis on primary and secondary datasets 199 6.5: Illustration of accuracy of photo theme detection of primary and secondary

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LIST OF ABBREVIATIONS

D/A - Digital to Analogue

A/D - Analogue to Digital

AES - Advanced Encryption System

ASCII - American Standard Code for Information Interchange

CA - Certificate Authority

CIA - Confidentiality, Integrity, Availability

DCT - Direct Cosine Transformation

DCT - Discrete Contourlet Transform

DES - Data Encryption Standard

ERR - Equal Error Rate

IQM - Image Quality Measures

KMA - Known Message Attack

KOA - Known Original Attack

LSB - Least Significant Bit

PC - Program Change

PDF - Probability Density Function

PRNG - Pseudo-Random Number Generator

PSNR - Peak Signal to Noise Ratio

QIM - Quantization Index Modulation

RGB - Red,Green,Blue

ROC - Receiver operating characteristics

SVM - Support Vector Machine

TMDE - Total Minimal Decision Error

WOA - Watermarked Only Attack