1. Median Filtering Detection Using Edge Based Prediction Matrix The 10th IWDW, Atlantic City, New Jersey, USA 23~26 October 2011 School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, P.R. China Chenglong Chen, Jiangqun Ni 1

2. Outline 1.Background of Median Filtering (MF) Detection 2.Related Work on MF Detection 3.Proposed Method 4.Experimental Results 5.Conclusions 2

3. Outline 1.Background of Median Filtering (MF) Detection 2.Related Work on MF Detection 3.Proposed Method 4.Experimental Results 5.Conclusions 3

4. Background of Median Filtering (MF) Detection Digital image generation/consumption increases Digital image editing becomes easy and popular Digital image forensics Determine image origin, integrity, authenticity Detect the processing history or manipulating history 4

5. Background of Median Filtering (MF) Detection Image manipulations 1.malicious tampering: copy&move, image splicing... content-preserving manipulations: resampling, median filtering… Median filtering (MF) detection most of the existing forensic methods rely on some kind of linearity assumption serve as an anti-forensic technique to destroy such linear constraints example: the new resampling scheme reported by Kirchner 5 M. Kirchner and R. B ӧ hme, “Hiding traces of resampling in digital images”, IEEE 2008

6. 6 5% upsampling upsampling by 5% and postprocessing with a 5x5 median filter Background of Median Filtering (MF) Detection

7. Outline 1.Background of Median Filtering (MF) Detection 2.Related Work on MF Detection 3.Proposed Method 4.Experimental Results 5.Conclusions 7

8. Streaking artifacts: there exists a trend that the output pixels in a certain neighborhood would take the same value in median filtered image –detect MF in bitmap images –analyzed by the first-order difference Subtractive pixel adjacency matrix (SPAM) –detect MF in JPEG post-compressed images –the conditional joint distribution of first-order difference 8 Related Work (1): Kirchner's method M. Kirchner and J. Fridrich, “On Detection of Median Filtering in Digital Images”, SPIE 2010

9. 9 Related Work (2): Cao's method G. Cao, et al., “Forensic detection of median filtering in digital images”, ICME 2010 first-order difference map original median filtered The probability of zero values on the first-order difference map of textured regions –another measurement of streaking artifacts

10. Kirchner's method and Cao’s method –Based on the first-order difference –Streaking artifacts is not robust to JPEG post-compression –The SPAM features is not clear enough. Contributions of our work –Another fingerprint of MF——EBPM –Improved robustness against JPEG post-compression 10 Related Work and Our Contributions

11. Outline 1.Background of Median Filtering (MF) Detection 2.Related Work on MF Detection 3.Proposed Method 4.Experimental Results 5.Conclusions 11

12. Good edge preservation of MF 12 Proposed MF Detection Scheme (a) idealized noisy edge (b) 5x5 median filter output (c) 5x5 average filter output (d) 5x5 gaussian filter output with σ =1.5

13. Step 1: Edge Block Classification –Divide the image into blocks –Classify into three types based on its gradient features oH: G V -G H >T oV: G H - G V >T oO: rest blocks 13 Proposed MF Detection Scheme

14. Step 2: Extraction of EBPM Features –Apply the same prediction model to all the blocks of the same type and estimate the prediction coefficients –Extract all the estimated prediction coefficients as the Edge Based Prediction Matrix(EBPM) Step 3: MF Detector via SVM 14 Proposed MF Detection Scheme

15. Outline 1.Background of Median Filtering (MF) Detection 2.Related Work on MF Detection 3.Proposed Method 4.Experimental Results 5.Conclusions 15

16. 16 Intuitive Efficiency of EBPM: αof Lena Intuitive Efficiency of EBPM: α H of Lena 1.the difference between and in (b) is greater than others, due to the effect of noise suppression and good edge preservation of MF 2.the difference becomes much smaller in (c) and (d) because the linear filters tend to smooth edges (a) (c) (b) (d)

17. 17 Intuitive Efficiency of EBPM : PCA Projections of 72-D EBPM features extracted from different types of sample images using UCID database onto a 2-D subspace spanned with top two PCA components. (a) (c) (b) (d)

18. 18 Distinguish MF from Original N: manipulated original images, P: manipulated median filtered images With other manipulations after MF (Robustness) –significant performance improvements for JPEG post- compression, compared to the streaking artifacts (b) (c) (a)

19. Distinguish MF from linear filter –Without JPEG post-compression –With JPEG post-compression 19 Distinguish MF from Other Manipulations N: linear filtered images, P: median filtered images

20. Outline 1.Background of Median Filtering (MF) Detection 2.Related Work on MF Detection 3.Proposed Method 4.Experimental Results 5.Conclusions 20

21. Good edge preservation of MF EBPM features –neighborhood prediction model –efficient and robust Improved MF detection performance Future work –extend forensic capability to other filters, especially other non-linear filters. –considering the edge in all orientation, a better model is needed for Step1: Edge Block Classification 21 Summary

22. Median Filtering Detection Using Edge Based Prediction Matrix The 10th IWDW, Atlantic City, New Jersey, USA 23~26 October 2011 School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, P.R. China Chenglong Chen, Jiangqun Ni Ph: 86-20-84036167 E-mail: c.chenglong@gmail.com, issjqni@mail.sysu.edu.cn 22