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
Outline
1.
2.
3.
4.
5.
Background of Median Filtering (MF) Detection
Related Work on MF Detection
Proposed Method
Experimental Results
Conclusions
2
Outline
1.
2.
3.
4.
5.
Background of Median Filtering (MF) Detection
Related Work on MF Detection
Proposed Method
Experimental Results
Conclusions
3
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
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
M. Kirchner and R. Bӧhme, “Hiding traces of resampling in digital images”, IEEE 2008
5
Background of Median Filtering (MF) Detection
5% upsampling
upsampling by 5% and postprocessing with a 5x5 median filter
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Outline
1.
2.
3.
4.
5.
Background of Median Filtering (MF) Detection
Related Work on MF Detection
Proposed Method
Experimental Results
Conclusions
7
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
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
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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
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Outline
1.
2.
3.
4.
5.
Background of Median Filtering (MF) Detection
Related Work on MF Detection
Proposed Method
Experimental Results
Conclusions
11
Proposed MF Detection Scheme
60
50
50
40
40
30
20
10
10
Amplitude
60
0
0
Position (j)
60
50
50
40
40
30
20
10
10
0
Position (j)
0
Position (j)
(d)
5x5 gaussian filter
output with σ=1.5
30
20
0
(b)
5x5 median
filter output
30
20
0
(c)
5x5 average
filter output
Amplitude
60
Amplitude
(a)
idealized
noisy edge
Amplitude
 Good edge preservation of MF
0
0
Position (j)
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Proposed MF Detection Scheme
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 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
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Outline
1.
2.
3.
4.
5.
Background of Median Filtering (MF) Detection
Related Work on MF Detection
Proposed Method
Experimental Results
Conclusions
15
Intuitive Efficiency of EBPM: αH of Lena
(a)
(b)
(c)
(d)
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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Intuitive Efficiency of EBPM : PCA
(a)
2
2
1
1
0
0
original
med=3X3
med=5X5
med=7X7
med=9X9
-1
-2
-3
2
(c)
-2
-1
0
1
2
-2
-3
2
1
0
0
-2
-3
delt=0.5
delt=1.5
med=3X3
med=5X5
med=7X7
med=9X9
-2
-1
0
1
avg=3X3
avg=5X5
med=3X3
med=5X5
med=7X7
med=9X9
-1
1
-1
(b)
-2
-1
0
-2
-3
2
(d)
s=0.8
s=1.2
med=3X3
med=5X5
med=7X7
med=9X9
-1
2
1
-2
-1
0
1
2
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 MF from Original
 With other manipulations after MF (Robustness)
–
significant performance improvements for JPEG postcompression, compared to the streaking artifacts
1
0.9
0.8
1
0.7
0.98
0.96
TP
0.6
(a)
(b)
avg=3X3
avg=5X5
delt=0.5
delt=1.5
s=0.8
s=1.2
QF=55
QF=75
QF=95
0.94
0.5
0.92
0.4
0.9
0.3
0
0.2
0.05
0.1
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
(c)
1
FP
N: manipulated original images, P: manipulated median filtered images
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Distinguish MF from Other Manipulations
 Distinguish MF from linear filter
–
–
Without JPEG post-compression
With JPEG post-compression
1
1
0.9
0.95
0.8
1
avg=3X3,QF=55
avg=3X3,QF=75
avg=3X3,QF=95
avg=5X5,QF=55
avg=5X5,QF=75
avg=5X5,QF=95
delt=0.5,QF=55
delt=0.5,QF=75
delt=0.5,QF=95
delt=1.5,QF=55
delt=1.5,QF=75
delt=1.5,QF=95
0.7
0.99
0.85
0.98
0.97
0.8
0
5
10
-3
0.75
0.7
15
x 10
0.98
0.5
0.02
0.04
0.06
FP
0.08
0.1
0.12
0.96
0.4
0.94
0.3
0.92
0.9
0.2
0
0.1
0
0
1
0.6
avg=3X3
avg=5X5
delt=0.5
delt=1.5
s=0.8
s=1.2
QF=55
QF=75
QF=95
TP
TP
0.9
0.14
0
0.1
0.05
0.2
0.3
0.1
0.4
0.5
0.6
0.7
0.8
0.9
1
FP
N: linear filtered images, P: median filtered images
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Outline
1.
2.
3.
4.
5.
Background of Median Filtering (MF) Detection
Related Work on MF Detection
Proposed Method
Experimental Results
Conclusions
20
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
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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: [email protected],
[email protected]
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