Measures for Classification and Detection in Steganalysis Sujit

Measures for Classification and Detection in Steganalysis Sujit

Measures for Classification and Detection
in Steganalysis
Sujit Prakash Gujar and C E Veni Madhavan
Department of Computer Science and Automation, Indian Institute of Science, Bangalore 560012, India.
Keywords
Steganography : Secret Communication
Steganalysis : Seeing the unseen
LSB Hiding, Support Vector Machines, Wavelets

Hide4PGP

Statistical and Pattern Classification Techniques
: Statistical feature vector. ( R9 )
captures different statistical properties of strings such as k-gram
frequencies, run lengths, auto-correlation and entropy like k-gram
frequencies, entropies.
First step : Classification of non-random data using and SVMs.
Use of 8 different file types : Accuracy 82.22%
1. Jpeg files 2. bmp/pnm files 3. zip files 4. gz files
5. text files 6. ps files 7. pdf files and 8. c files.
Classification of LSB plane, stegoed and non-stegoed image : Accuracy 85%
Classification of LSB plane as 4 class problem : Accuracy 65 %.
LSB planes of
1. non-Stegoed image. 2. 25% stegoed image.
3. 50% Stegoed image. 4. 75% stegoed image.

CSA Tool

Wavelets
Image properties are generally captured more accurately in 2-D transforms
I = Set of Cover (non stegoed) Images.
: (# { W( Ski ) W( Sk ) 0})*500/(Image size)})*50})*500/(Image size)0})*500/(Image size)/(Image size)
ki = Average over different images I when
k % of embedding is present and i % forced embedding is done.
Experiments are performed on Hide4PGP and CSA-Tool (Simulated S-Tool)
Graph 1 : ki vs i for various values of k.
Graph 2 : vs k at fixed i for various images.

Hide4PGP

(Stegoed Object)

Cover

Graph 1

Ski

Start Image Sk
Forced i %
embedding

Wavelet Transform
Secret Message

(2nd Level LL Sub band)

k % embedding

CSA Tool

Get from difference count

Conclusion
Two of our approaches towards analysis of stego images for detection of
levels of embedding have been discussed. Our approach of using wavelet
coefficient perturbations holds promise. We also would consider a modified
wavelet coefficient based measure that takes into account the numerical
changes in the pixel values introduced by embedding. We plan to use this
measure in addition to the statistical measures to arrive at finer detection.

Graph 2

Presented at 3rd Workshop on Computer Vision, Graphics, and Image Processing (WCVGIP) 12-13 Jan. 2006, Hyderabad, India.

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