Presentation is loading. Please wait.

Presentation is loading. Please wait.

Steganalysis with Streamwise Feature Selection Steven D. Baker University of Virginia

Similar presentations


Presentation on theme: "Steganalysis with Streamwise Feature Selection Steven D. Baker University of Virginia"— Presentation transcript:

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

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

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 Triangle of Peril Secrecy Robustnes s Rate Detectable Useless Target region

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

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) ? Reject feature Decrease wealth Add feature Increase wealth More features, more time? Generate feature

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?


Download ppt "Steganalysis with Streamwise Feature Selection Steven D. Baker University of Virginia"

Similar presentations


Ads by Google