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Advancing Steganalysis through Streamwise Feature Selection

Explore the intricate world of steganography and steganalysis, focusing on feature selection methods to enhance detection capabilities without compromising legitimate image sharing. Dive into theoretical guarantees, robustness, and the Triangle of Peril concept. Delve into the Streamwise Feature Selection approach and its impact on improving detection accuracy. Discover the blend of existing features to create novel ones for effective steganalysis. Unravel the complexities of steganographic techniques and their implications on security measures.

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Advancing Steganalysis through 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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