Steganalysis with Streamwise Feature Selection Steven D. Baker
Steganalysis with Streamwise Feature Selection Steven D. Baker University of Virginia [email protected] Steganography: An Example Original Image Hello, I am the amazing Mr.
Moulin! Hello, I am the amazing Mr. Moulin 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 Triangle of Peril
Robustness Target region Detectable Useless Secrecy Rate Theoretical work in Steganography
Complexity theory Information theory
Provably Secure Steganography [Hopper et al.] 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* Method of Wang and Moulin
Steg and cover images Training Classification Wavelet transform Feature Selection PDF Characteristic Function
Moments (Feature generation) 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.] Feature generation/selection (Alpha-investing) Add feature Reject feature Decrease wealth ? More features, more time?
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Explore! New vocabulary:. Dominant. form of a trait that will be expressed if an allele for the trait is present. Recessive. form of a trait that will be expressed only in the absence of a dominant allele
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