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Steganalysis with Streamwise Feature Selection

This study explores advanced techniques in steganalysis using streamwise feature selection to improve the detection of hidden communications in images. By addressing the challenges of distinguishing between legitimate and secret communications, we aim to enhance detection methods without compromising image integrity. The research highlights theoretical frameworks, feature generation, and selection methods, pinpointing potential improvements in robust image analysis. Experimental data from CorelDraw images are utilized to evaluate model complexity, detection effectiveness, and the balance between secrecy and robustness.

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Steganalysis with Streamwise Feature Selection

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  1. Steganalysis with Streamwise Feature Selection Steven D. Baker University of Virginia sdb7e@cs.virginia.edu

  2. Steganography: An Example “Hello, I am the amazing Mr. Moulin!” “Hello, I am the amazing Mr. Moulin…” Original Image

  3. Motivation • Catch bad people trying to communicate in secret • Catch good people trying to communicate in secret? • Research opportunities: • Improve detection • Disrupt secret communication without harming legitimate image sharing • Improve theoretical guarantees

  4. Robustness Secrecy Rate Triangle of Peril Target region Detectable Useless

  5. Theoretical work in Steganography • Complexity theory • Provably Secure Steganography [Hopper et al.] • Information theory • An Information-Theoretic Model for Steganography [Cachin] • Perfectly Secure Steganography [Wang and Moulin] • Basic conclusion: perfect security means useless rate • No available, practical algorithm allows smooth adjustment of rate, robustness, and secrecy*

  6. PDF Characteristic Function Wavelet transform Training Classification Moments (Feature generation) Feature Selection Method of Wang and Moulin Steg and cover images

  7. Let Intel do the work • Can we combine existing features to form useful new features? • Have a computer separate the useless features from the good ones • Do this in a suboptimal but very fast way, so that you can evaluate loads of features, more than there are observations • Streamwise Feature Selection [Zhou et al.]

  8. ? Feature generation/selection(Alpha-investing) Add feature Generate feature Reject feature Decrease wealth Increase wealth More features, more time?

  9. Feature generation • Pair-wise ratios • Pair-wise differences • Principal Component Analysis • Untested possibilities: • Log • Square root • Nonsmooth Nonnegative Matrix Factorization

  10. Experimental data (Shi et al.) • CorelDraw images • ~1000 images • 78 Original features • > 6000 generated features • Steganographic techniques • Cox et al. (SS) • Piva et al. (SS) • Huang et al. (SS) • Generic QIM • Generic LSB

  11. Results: ROC

  12. Results: Model Complexity

  13. Results: AUC

  14. Questions?

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