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Grey Level Modification Steganography for Secret Communication

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Title: Grey Level Modification Steganography for Secret Communication


1
Grey Level Modification Steganographyfor Secret
Communication
  • V. POTDAR and E. CHANG
  • M.Adnan KHAN
  • Curtin University of Technology
  • Perth Western Australia

2
Motivation
  • Security, Privacy, Anti-virus, Trusted computing,
    intrusion detection and information protection
  • Critical components for industrial based IT
    solutions
  • Third party vendors for protection
  • Issues
  • Trust of the third parties
  • one industrial system, may have several trustees
  • Solutions
  • Use own security guard

3
Proposed Solution
  • Steganography for security and information
    protection
  • an information hiding technique
  • without a need for third party trustees
  • Secret Communication

4
Steganography
  • Methods of transmitting secret messages
    through Carriers such as images, audio, video,
    text, or any other digitally represented code.
  • The hidden message may be plaintext,
    ciphertext, or any-thing that can be represented
    as a bit stream.
  • The problem
  • Can be detected by careful statistical
    analysis

5
Challenges
  • the secrecy of the cover medium
  • the robustness of the algorithm used
  • To protect secrecy, we need to
  • - discover new and better cover mediums or
  • - design and develop robust algorithms
  • The difficulties
  • (a) information embedding capacity and
  • (b) robustness of algorithms against detection

6
Existing Research
  • The existing research shows that the best way
    is to oversee the level of modification that is
    made to the cover media.
  • IF modified too much the statistical changes are
    evident and such changes can indicate the use of
    steganography
  • IF cannot be intelligently modified in a most
    secret way, it results in less embedding capacity

7
Existing Research
  • Existing Steganographic mediums and techniques
    suffer from a myriad of attacks on images, video
    and audio Johnson and Jajodia
  • Great effort to defend the attacks by improving
    cover medium OR communication protocols.
  • Defending techniques through covered medium
  • Defending techniques through protocols

8
Existing Research
  • Defending techniques through covered medium
  • Replacing Least Significant Bit Chen 2001, Lee
    et al. 2000
  • Replacing Moderate Significant Bit Chan and
    Chang 2001
  • Pixel Modification Techniques Zincheng et al.
    2003, Wu et al. 2003, Xinpeng and Shuozhong,
    2003, Soo-Chang and Jing-Ming, 2003 Zincheng et
    al. 2003 etc.
  • Defending techniques through protocols
  • the weaknesses of the TCP/IP Fisk et al. 2002
  • email headers Bao et al. 2002
  • the discrete cosine transform (DCT) and the
    discrete wavelet transform (DWT) Chang et al.
    2002, and Hsu et al. 1999.

9
Existing Research
  • this scheme is sensitive to a variety of image
    processing attacks like compression, cropping
  • it degrades the quality of stego-image
  • trade off high embedding rate
  • vulnerable to steganalysis- based on histogram of
    pixel value differences

10
A New Steganographic Approach
  • embeds data or information within the spatial
    domain of the greyscale images by modifying the
    grey level values of the pixels.

11
Preliminary Concepts
  • A digital image f(x, y), When the values x, y and
    f are finite values then we call such an image a
    digital image.
  • A image pixel is a definite location and
    amplitude that compose a image.
  • Grey scale is Grey Level Values, a sequence of
    grey shades from black to white with intermediate
    shades of grey. It has a finite range 0, 2N-1,
  • an 8 bit grey scale image range is from 0 to 255.

12
Preliminary Concepts
  • Grey Level Modification is defined as a technique
    in which the grey level values of the image
    pixels are modified in accordance with a
    mathematical function, to represent binary data.
    Each pixel has a distinct grey level value which
    can have an odd or even value. This odd or even
    value of the grey level is appropriately modified
    to represent binary data.

13
Grey Level Modification Steganography (GLMS)
  • GLMS is a technique in which the grey level
    values of the image pixels are modified in
    accordance with a mathematical function, to
    represent binary data.
  • Each pixel has a distinct grey level value which
    can have an odd or even value. This odd or even
    value of the grey level is appropriately modified
    to represent binary data.
  • GLM Steganography uses the concept of odd and
    even numbers to map data within an image. It is a
    one-to-one mapping between the binary data and
    the selected pixels in an image.

14
Grey Level Modification Algorithm
15
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18
Formal Definition of GLM Algorithm
GLM Steganography modifies the value of l at a
coordinate (x0, y0) by n units based on an
arbitrary function g, which selects z number of
pixels from the matrix f(x, y) to map it to a bit
stream B of length .
19
GLM Steganography Prototype Initial Screen
Implementation
20
Java Code
The prototype is implemented using java. We
created 18 java classes. The screenshot shows the
ImageMain class and the adjoining classes
21
Java Code
Binary1.java class. This class converts textual
data into binary format.
22
This prototype has been tested on Jpeg, Gif and
BMP images to hide data
Image properties are shown here.
23
The text to be hidden is entered in this text area
The text is first converted into binary format
which is then mapped with the image pixels.
24
Grey Level Modification Steganography
The initial and final pixel values are shown
which are changed based on the bit that is mapped
with it
25
The prototype has been tested on 1088 characters
or 7616 bits
26
The pixel selection is based on a key. The
original and the modified values of the selected
pixels are shown in the text area
27
Grey Level Modification Steganography
The modified image is saved as lena2.bmp
28
Comparison of the histograms of the original and
modified image
There is very minute difference in the two
histograms which is not noticeable.
29
Advantages
  • It has low computational complexity
  • high information hiding capacity
  • The principle of the algorithm is to embed
    information by modifying the grey level values of
    the grey scale image pixels.
  • We demonstrate this method by giving examples,
    implementation of prototype systems as proof of
    the concepts.

30
Future Work
  • Increase the embedding capacity.
  • Prevent data loss from image modification like
    cropping, format change etc.

Thank you!
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