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Steganalysis with Streamwise Feature Selection Steven D. Baker University of Virginia [email protected]
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Steganalysis with Streamwise Feature Selection

Feb 10, 2016

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Steganalysis with Streamwise Feature Selection. Steven D. Baker University of Virginia [email protected]. Steganography: An Example. “Hello, I am the amazing Mr. Moulin!”. “Hello, I am the amazing Mr. Moulin…”. Original Image. Motivation. - PowerPoint PPT Presentation
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Page 1: Steganalysis with Streamwise Feature Selection

Steganalysis with Streamwise Feature

Selection

Steven D. BakerUniversity of Virginia

[email protected]

Page 2: Steganalysis with Streamwise Feature Selection

Steganography: An Example

Original Image “Hello, I am the amazing Mr. Moulin!”

“Hello, I am the amazing Mr. Moulin…”

Page 3: Steganalysis with Streamwise Feature Selection

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

Page 4: Steganalysis with Streamwise Feature Selection

Triangle of Peril

Secrecy

Robustness

Rate

Detectable

Useless

Target region

Page 5: Steganalysis with Streamwise Feature Selection

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*

Page 6: Steganalysis with Streamwise Feature Selection

Method of Wang and Moulin

Steg and coverimages Wavelet transform

PDFCharacteristic Function

Moments(Feature generation)Feature SelectionTrainingClassification

Page 7: Steganalysis with Streamwise Feature Selection

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.]

Page 8: Steganalysis with Streamwise Feature Selection

Feature generation/selection(Alpha-investing)

?

Reject featureDecrease wealth

Add feature

Increase wealth

More features, more time?

Generate feature

Page 9: Steganalysis with Streamwise Feature Selection

Feature generation Pair-wise ratios Pair-wise differences Principal Component Analysis Untested possibilities:

Log Square root Nonsmooth Nonnegative Matrix Factorization

Page 10: Steganalysis with Streamwise Feature Selection

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

Page 11: Steganalysis with Streamwise Feature Selection

Results: ROC

Page 12: Steganalysis with Streamwise Feature Selection

Results: Model Complexity

Page 13: Steganalysis with Streamwise Feature Selection

Results: AUC

Page 14: Steganalysis with Streamwise Feature Selection

Questions?