Measures for Classification and Detection in Steganalysis

1. Measures for Classification and Detection in Steganalysis Hide4PGP CSA Tool Graph 1 Hide4PGP CSA Tool 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 • Statistical and Pattern Classification Techniques • μ : Statistical feature vector. ( μ Є R9) • μ 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 μand 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. • Wavelets • Image properties are generally captured more accurately in 2-D transforms • I = Set of Cover (non stegoed) Images. • η : (# { W( Ski ) – W( Sk ) ≠ 0})*500/(Image size) • ηki= Average η over different images ЄI 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 : ηki vs ‘i’ for various values of ‘k’. • Graph 2 : η vs ‘k’ at fixed ‘i’ for various images. (Stegoed Object) Start Image Sk Ski Cover Forced i%embedding Wavelet Transform (2nd Level LL Sub band) Secret Message k % embedding Get ‘η’ 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.