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Steganography and Steganalysis of JPEG Images · PDF file JPEG Steganography 1 Introduction Steganography is a technique to hide data inside a cover medium in such a way that the existence

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  • DEPARTMENT OF COMPUTER AND INFORMATION

    SCIENCES AND ENGINEERING

    PH.D. PROPOSAL

    Steganography and Steganalysis of JPEG Images

    Author:

    Mahendra Kumar

    [email protected]

    Supervisory Committee:

    Dr. Richard E. Newman (Chair)

    Dr. Jonathan C. L. Liu (Co-Chair)

    Dr. Randy Y. C. Chow

    Dr. José A.B. Fortes

    Dr. Liquing Yang

    January 15, 2011

  • Preface

    1 Research Motivation

    My research motivation came from a project supported by Naval Research Laboratory (NRL) where I was

    working on an algorithm to provide better stealthiness for hiding data inside JPEG images. As a result, with

    the guidance of my advisor, Dr. Newman, and Ira S. Moskowitz from Center for High Assurance Computer

    Systems, NRL, we developed J2 steganography algorithm which was based on hiding data in the spatial

    domain by making changes in the frequency domain. J2 had problems such as lower capacity along with no

    first order histogram restoration. This led to the development of J3 where the global histogram is preserved

    along with higher capacity. But, the first order preservation is not enough since it can be detected using

    higher order statistics. I plan to develop an algorithm where I could maintain the first and second order

    statistics in stego images with respect to cover image. In order to develop a good steganography algorithm,

    one should have knowledge about the different steganalysis techniques. Keeping this in mind, I also plan to

    propose a steganalysis scheme where I would estimate the cover image using the second order statistics.

    1.1 Research goals

    My research goals focus on the following topics:

    1. Designing a frequency based embedding approach with spatial based extraction using hash of the data

    from spatial domain, J2. (Done)

    2. Designing a novel approach to high capacity JPEG steganography using histogram compensation

    technique, J3. (Done)

    3. Designing a JPEG steganography algorithm using first and second order statistical restoration tech-

    niques with high performance in terms of steganalysis, J4. (Work in progress)

    1

  • 4. Designing a steganalysis scheme based on estimation of cover using the second order statistics. (Work

    in progress)

    5. Improvement over features of J2 and J3 and analyzing more experimental results for steganalysis

    using Support Vector Machines. (Work in Progress)

    2 Contribution

    We developed two techniques to embed data in the JPEG medium. The first one, called J2, embeds data

    by making changing to the DCT coefficients which in turn makes changes in the spatial domain values.

    The extraction is done by converting JPEG to spatial domain and hashing the values of the bits from the

    color pixels. Second algorithm, which was a great improvement over J2, called J3, has a high capacity and

    it embeds data with great efficiency and better stealthiness. It also has the ability to restore the histogram

    completely to its original values. The third algorithm, as proposed in the future work section 5, would be

    focussed on development of steganography algorithm which would be capable of restoring first as well as

    second order statistics. Work on completing restoring second order statistics has not be done before which

    if done would be an important tool for steganography and would provide high stealthiness as compared to

    other existing algorithms. I also plan to develop a steganalysis schemes based on estimation of cover image

    using second order statistics. This type of estimation has not been done before and if successful would be

    an important tool in the field of steganalysis.

    2

  • 3

  • Acknowledgements

    I am heartily thankful to my advisor, Dr. Richard Newman, whose encouragement, guidance and support

    enabled me to develop an understanding of this area of research and completion of my proposal. I would

    also like to thank Dr. Ira S. Moskowitz (Center for High Assurance Computer Systems, Naval Research

    Laboratory), who gave us valuable input and feedback towards development of J2 and J3.

    Finally, I would like to show my deepest gratitude to my committee members, Dr. Jonathan Liu, Dr.

    José Fortes and Dr. Randy Chow from Department of Computer & Information Sciences and Engineering

    (CISE), and Dr. Liquing Yang from Department of Electrical & Computer Engineering, for their support,

    guidance and novel ideas towards my research.

    4

  • Contents

    Preface 1

    1 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    1.1 Research goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

    Acknowledgements 3

    Contents 5

    List of Figures 9

    List of Tables 10

    1 JPEG Steganography 11

    1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

    2 JPEG Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

    3 JPEG Steganography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

    3.1 LSB-Based Embedding Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

    4 Popular Steganography Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

    4.1 JSteg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

    4.2 F5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

    4.3 Outguess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

    4.4 Steghide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

    4.5 Spread Spectrum Steganography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

    5

  • 4.6 Model Based Steganography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

    4.7 Statistical Restoration Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

    2 JPEG Steganalysis 21

    1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

    2 Pattern Recognition Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

    2.1 JPEG Steganalysis using SVMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

    3 Steganalysis using Second order statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

    3.1 Markov Model Based Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

    3.2 Merging Markov and DCT features . . . . . . . . . . . . . . . . . . . . . . . . . . 26

    3.3 Other second order statistical methods . . . . . . . . . . . . . . . . . . . . . . . . . 28

    3 J2: Refinement Of A Topological Image Steganographic Method 31

    1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

    2 Review of J1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

    2.1 Algorithm in brief . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

    3 Motivation for Probabilistic Spatial Domain Stego-embedding . . . . . . . . . . . . . . . . 34

    4 J2 Stego Embedding Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

    4.1 J2 Algorithm in Detail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

    5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

    6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

    4 J3: High Payload Histogram Neutral JPEG Steganography 46

    1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

    2 J3 Embedding Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

    2.1 Embedding Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

    3 J3 Extraction Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

    3.1 Extraction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

    4 Estimation of Embedding Capacity and Stop Point . . . . . . . . . . . . . . . . . . . . . . 58

    4.1 Stop Point Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

    4.2 Capacity Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

    6

  • 5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

    5.1 Estimated Capacity vs Actual Capacity . . . . . . . . . . . . . . . . . . . . . . . . 66

    5.2 Estimated Stop-Point vs Actual Stop-Point . . . . . . . . . . . . . . . . . . . . . . 67

    5.3 Embedding Efficiency of J3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

    5.4 Comparison of J3 with other algorithms . . . . . . . . . . . . . . . . . . . . . . . . 69

    6 Steganalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

    6.1 Binary classification . . . . . . . . . .