Revisiting Weighted Stego-Image Steganalysis

Transcription

1 Revisiting Weighted Stego-Image Steganalysis Andrew Ker comlab.ox.ac.uk Oxford University Computing Laboratory Rainer Böhme rainer.boehme@ inf.tu-dresden.de Technische Universität Dresden, Institute for System Architecture SPIE/IS&T Electronic Imaging, San Jose, CA 28 January 2008

2 Revisiting Weighted Stego-Image Steganalysis Outline The Weighted Stego Image (WS) method Performance Re-engineering WS Performance WS for sequential embedding Performance

3 The WS Method Imagine 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.

4 The WS Method Cover image: Stego image: Weighted stego image : (real-valued) Flip proportion of LSBs Move towards flipping all LSBs Theorem [Fridrich & Goljan, 2004] The function is minimized at, where the are a vector of weights.

5 The WS Method Theorem 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. Average of the four stego pixels neighbouring is the local variance of the four stego pixels neighbouring estimate of bias introduced by flat areas in cover image Estimate proportionate payload size

6 Performance SPA Couples/ML Leading structural detectors for LSB replacement in never-compressed covers Mean asbolute error of estimator True proportionate payload Cover source: 3000 grayscale scanned images resampled to 0.3Mpixels

7 Performance Mean asbolute error of estimator SPA Couples/ML WS, unweighted WS, with weighting WS, with weighting & flat-pixel correction True proportionate payload Cover source: 3000 grayscale scanned images resampled to 0.3Mpixels

8 Adaptive Cover Predictors Estimate cover by filtering the stego image. Average of the four stego pixels neighbouring

9 Adaptive Cover Predictors Estimate cover by filtering the stego image. But what about other filters?

10 Adaptive Cover Predictors Estimate cover by filtering the stego image. But what about other filters?

11 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.

12 Moderated Weights Decide on a weight vector. is the local variance of the four stego pixels neighbouring Our experiments suggested that the weights are too extreme and should be moderated. is the weighted variance of the neighbouring stego pixels affecting in the prediction filter improves payload size estimation accuracy.

13 Bias Correction Correct bias. The flat-pixel correction in [Fridrich & Goljan, EI 2004], doesn t work very well. A better estimate can be given if we model the cover image by Flip proportion of LSBs Then improves payload size estimation accuracy.

14 Re-engineered WS Theorem The function is minimized at. WS Steganalysis 1. Estimate cover by filtering the stego image. [ 2. Decide on a weight vector. Find F to minimize then is the local variance of the neighbouring stego pixels affecting in the prediction filter 3. Compute bias correction. Estimate proportionate payload size

15 Performance Mean asbolute error of estimator SPA Couples/ML Standard WS Improved WS True proportionate payload Cover source: 1600 grayscale RAW digital camera images cropped to 0.3Mpixels

16 Performance Mean asbolute error of estimator SPA Couples/ML Standard WS Improved WS True proportionate payload Cover source: 1600 grayscale RAW digital camera images resampled to 0.3Mpixels

17 Performance Mean asbolute error of estimator SPA Couples/ML Standard WS Improved WS True proportionate payload Cover source: 3000 grayscale scanned images resampled to 0.3Mpixels

18 WS For Sequential Payload Cover image: Stego image: Weighted stego image: Flip first M LSBs with probability Go halfway to flipping first LSBs 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.

19 Performance Mean asbolute error of estimator SPA Sequential Chi-Square Standard WS Sequential WS (basic) Sequential WS (improved) True proportionate payload Cover source: 1000 digital camera images, cropped to 0.3Mpixels

20 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 sequentiallylocated 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.

21 End