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

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OutlineOutline

1. Background of Median Filtering (MF) Detection2. Related Work on MF Detection3. Proposed Method4. Experimental Results5. Conclusions

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OutlineOutline

1. Background of Median Filtering (MF) Detection2. Related Work on MF Detection3. Proposed Method4. Experimental Results5. Conclusions

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Background of MBackground of Median edian FFiltering iltering (MF) Detection(MF) Detection

Digital image generation/consumption increasesDigital image editing becomes easy and popular

Digital image forensics• Determine image origin, integrity, authenticity • Detect the processing history or manipulating history

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Background of MBackground of Median edian FFiltering iltering (MF) Detection(MF) Detection

Image manipulations1. 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

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M. Kirchner and R. Bӧhme, “Hiding traces of resampling in digital images”, IEEE 2008

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5% upsampling

upsampling by 5% and postprocessing with a 5x5 median filter

Background of MBackground of Median edian FFiltering iltering (MF) Detection(MF) Detection

OutlineOutline

1. Background of Median Filtering (MF) Detection2. Related Work on MF Detection3. Proposed Method4. Experimental Results5. Conclusions

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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

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Related Work (1): Kirchner's methodRelated Work (1): Kirchner's method

M. Kirchner and J. Fridrich, “On Detection of Median Filtering in Digital Images”, SPIE 2010

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Related Work (2): Cao's methodRelated 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

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

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Related Work and Our ContributionsRelated Work and Our Contributions

OutlineOutline

1. Background of Median Filtering (MF) Detection2. Related Work on MF Detection3. Proposed Method4. Experimental Results5. Conclusions

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Good edge preservation of MF

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Proposed MF Detection SchemeProposed MF Detection Scheme

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(a)idealized

noisy edge

(b)5x5 medianfilter output

(c)5x5 averagefilter output

(d)5x5 gaussian filteroutput with σ=1.5

Step 1: Edge Block Classification– Divide the image into blocks– Classify into three types based on its gradient features

o H: GV-GH>T

o V: GH- GV>T

o O: rest blocks

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Proposed MF Detection SchemeProposed MF Detection Scheme

Step 2: Extraction of EBPM Features– Apply the same prediction model to all the blocks of the s

ame 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

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Proposed MF Detection SchemeProposed MF Detection Scheme

OutlineOutline

1. Background of Median Filtering (MF) Detection2. Related Work on MF Detection3. Proposed Method4. Experimental Results5. Conclusions

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Intuitive Efficiency of EBPMIntuitive Efficiency of EBPM: α: αH of Lenaof 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

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(c)

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Intuitive Efficiency of EBPM : Intuitive Efficiency of EBPM : PCA PCA

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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.

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Distinguish Distinguish MF from OriginalMF from Original

N: manipulated original images, P: manipulated median filtered images

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avg=3X3avg=5X5delt=0.5delt=1.5s=0.8s=1.2QF=55QF=75QF=95

With other manipulations after MF (Robustness)– significant performance improvements for JPEG post-com

pression, compared to the streaking artifacts

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(c)

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Distinguish MF from linear filter– Without JPEG post-compression– With JPEG post-compression

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Distinguish Distinguish MF from OMF from Other Manipulationsther Manipulations

N: linear filtered images, P: median filtered images

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avg=3X3,QF=55avg=3X3,QF=75avg=3X3,QF=95avg=5X5,QF=55avg=5X5,QF=75avg=5X5,QF=95delt=0.5,QF=55delt=0.5,QF=75delt=0.5,QF=95delt=1.5,QF=55delt=1.5,QF=75delt=1.5,QF=95

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OutlineOutline

1. Background of Median Filtering (MF) Detection2. Related Work on MF Detection3. Proposed Method4. Experimental Results5. Conclusions

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Good edge preservation of MFEBPM features

– neighborhood prediction model– efficient and robust

Improved MF detection performanceFuture 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

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SummarySummary

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: 86-20-84036167E-mail: c.chenglong@gmail.com, issjqni@mail.sysu.edu.cn

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