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Revisiting Weighted

Stego-Image Steganalysis

Andrew Keradk@comlab.ox.ac.uk

Oxford University Computing Laboratory

Rainer Bhmerainer.boehme@inf.tu-dresden.de

Technische Universitt Dresden,

Institute for System Architecture

SPIE/IS&T Electronic Imaging, San Jose, CA

28 January 2008

Outline

The Weighted Stego Image (WS) method

Performance

Re-engineering WS

Performance

WS for sequential embedding

Performance

Revisiting Weighted

Stego-Image Steganalysis

The WS MethodImagine a single-channel cover image with N pixels, and a payload of M bits

(possibly zero) inserted by overwriting a selection of LSBs.

WS steganalysis estimates the (proportionate) payload size .

The WS Method

Theorem [Fridrich & Goljan, 2004]

The function is minimized at ,

where the are a vector of weights.

Cover image:

Stego image:

Weighted stego image:

(real-valued)

Move towards flipping all LSBs

Flip proportion of LSBs

The WS MethodTheorem

The function is minimized at .

WS Steganalysis

1. Estimate cover by filtering

the stego image.

[

2. Decide on a weight vector.

3. Compute flat-pixel correction.

Estimate proportionate payload size

estimate of bias introduced by flat areas in cover image

Average of the four stego pixels neighbouring

is the local variance of the four stego pixels neighbouring

Performance

Cover source:

3000 grayscale scanned images resampled to 0.3Mpixels

Mean asbolute error of estimator

SPACouples/ML

True proportionate payload

Leading structural detectors for LSB

replacement in never-compressed covers

Performance

Cover source:

3000 grayscale scanned images resampled to 0.3Mpixels

Mean asbolute error of estimator

SPACouples/MLWS, unweightedWS, with weightingWS, with weighting & flat-pixel correction

True proportionate payload

Adaptive Cover Predictors Estimate cover by filtering the stego image.

Average of the four stego pixels neighbouring

Adaptive Cover Predictors Estimate cover by filtering the stego image.

But what about other filters?

Adaptive Cover Predictors Estimate cover by filtering the stego image.

But what about other filters?

Adaptive Cover Predictors Estimate cover by filtering the stego image.

Select a filter pattern

and find the values of a...e to best predict the stego object by itself, i.e. find

improves cover pixel & payload size estimation accuracy.

Moderated Weights Decide on a weight vector.

Our experiments suggested that the weights are too extreme and should be

moderated.

improves payload size estimation accuracy.

is the local variance of the four stego pixels neighbouring

is the weighted variance of the neighbouring stego pixels affecting in the prediction filter

Bias Correction Correct bias.

The flat-pixel correction in [Fridrich & Goljan, EI 2004], doesnt work very

well. A better estimate can be given if we model the cover image by

Then

improves payload size estimation accuracy.

Flip proportion of LSBs

Re-engineered WSTheorem

The function is minimized at .

WS Steganalysis

1. Estimate cover by filtering

the stego image.

[

2. Decide on a weight vector.

3. Compute bias correction.

Estimate proportionate payload size

Find F to minimizethen

is the local variance of the neighbouring stego pixels affecting

in the prediction filter

Performance

Cover source:

1600 grayscale RAW digital camera images cropped to 0.3Mpixels

Mean asbolute error of estimator

SPACouples/MLStandard WSImproved WS

True proportionate payload

Performance

Cover source:

1600 grayscale RAW digital camera images resampled to 0.3Mpixels

Mean asbolute error of estimator

SPACouples/MLStandard WSImproved WS

True proportionate payload

Performance

Cover source:

3000 grayscale scanned images resampled to 0.3Mpixels

Mean asbolute error of estimator

SPACouples/MLStandard WSImproved WS

True proportionate payload

WS For Sequential Payload

Theorem

The function is minimized at .

Sequential WS Steganalysis

1. Estimate cover by filtering stego image:[

2. Estimate size of payload:

Weighting can also be used.

Cover image:

Stego image:

Weighted stego image: Go halfway to flipping first LSBs

Flip first M LSBs with probability

Performance

Cover source:

1000 digital camera images, cropped to 0.3Mpixels

Mean asbolute error of estimator

SPASequential Chi-SquareStandard WSSequential WS (basic)Sequential WS (improved)

True proportionate payload

Conclusions WS, a steganalysis method for LSB replacement, received little attention.

Its performance was a little worse than structural detectors.

We upgraded its three components: cover prediction, weighting, and bias

correction.

For never-compressed covers, its performance is (almost always)

superior to any other detector, and its computational complexity is low.

There are simple modifications for specialized detection of sequentially-

located payload.

The performance here is orders of magnitude better than its competitors.

WS has been unjustly neglected and, because of its modular design, there

may be many other applications.

End

adk@comlab.ox.ac.ukadk@comlab.ox.ac.ukadk@comlab.ox.ac.ukadk@comlab.ox.ac.uk

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