Median Filtering Detection Using Edge Based Prediction Matrix

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

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

3. Outline Background of Median Filtering (MF) Detection Related Work on MF Detection Proposed Method Experimental Results 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 • 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 M. Kirchner and R. Bӧhme, “Hiding traces of resampling in digital images”, IEEE 2008 5

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

7. Outline Background of Median Filtering (MF) Detection Related Work on MF Detection Proposed Method Experimental Results Conclusions 7

8. Related Work (1): Kirchner's method • 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 M. Kirchner and J. Fridrich, “On Detection of Median Filtering in Digital Images”, SPIE 2010 8

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

10. Related Work and Our Contributions • 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

11. Outline Background of Median Filtering (MF) Detection Related Work on MF Detection Proposed Method Experimental Results Conclusions 11

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

13. Proposed MF Detection Scheme • Step 1: Edge Block Classification • Divide the image into blocks • Classify into three types based on its gradient features • H: GV-GH>T • V: GH- GV>T • O: rest blocks 13

14. Proposed MF Detection Scheme • 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

15. Outline Background of Median Filtering (MF) Detection Related Work on MF Detection Proposed Method Experimental Results Conclusions 15

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

17. Intuitive Efficiency of EBPM : PCA (a) (b) (c) (d) 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. 17

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

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

20. Outline Background of Median Filtering (MF) Detection Related Work on MF Detection Proposed Method Experimental Results Conclusions 20

21. Summary • 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

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