Camera Model Identification Based on
the Characteristics of CFA and
Interpolation
Shang Gao1, Guanshuo Xu2, Rui-Min Hu1,*
email.nancy.g@gmail.com, gx3@njit.edu, hrm1964@public.wh.hb.cn
1. Wuhan University, China; 2. New Jersey Institute of Technology, US
Table of Contents



Introduction – Camera Model Identification
Color filter Array (CFA) and Color Interpolation in Camera
Camera Model Detection







Artifacts brought by CFA and Interpolation (Idea of feature
extraction)
Feature Set 1
Feature Set 2
Feature Set 3
Feature Extraction Flow Diagram
Experiment
Conclusion
Introduction – Camera Model Identification


One category of digital image forensics
If the image we are interested in are captured by a digital camera,
then, what is the make and model of the camera?
CFA and Interpolation in Camera
(1/2)
•Fig.2. CFA pattern
interpolation
+
+
Demosaicing
image (RGB)
•Fig.1. CFA and interpolation processing
Images from Internet
CFA and Interpolation in Camera
(2/2)



The process of interpolation is to estimates lack color component
by its existing neighbor color components.
It can be regarded as a weighted average processing and is
similar as low pass filtering [8, 9, 16].
Theoretically, it leads to the fact that interpolated color
components are ‘smoother’ than the original part statistically.
0
0.25
0
0.25
1
0.25
0
0.25
0
•Fig.3. bilinear interpolation kernel (Green channel)



[8] H. Cao and A. C. Kot: Accurate detection of demosaicing regularity for digital image
forensics. IEEE Transactions on Information Forensics and Security 4(4), pp. 899-910 (2009)
[9] Matthias Kirchner: Efficient Estimation of CFA Pattern Configuration in Digital Camera
Images. Media Forensics and Security II, Proc. SPIE, Vol. 754110 (2010)
[16] A.E Dirik, N. Memon: Image tamper detection based on demosaicing artifacts. In: ICIP(09),
Cairo, Egypt
Artifacts brought by CFA and
Interpolation

We use equation proposed in [16]
r
e
var( N ) var( N )
m ax(
,
)
e
r
var( N ) var( N )

(1)
Consideration



We use this feature for camera model identification and expand the feature
according to different CFA patterns.
There are two pixel sets after interpolation: raw and estimated pixel set. The
‘smooth’ effect by interpolation will lead to different variances between these
two sets.
The statistical difference between raw pixels values and estimated pixel
values is hard to observe using pixel values directly, but it could be more
obvious by calculating the statistics of noise part.
[16] A.E Dirik, N. Memon: Image tamper detection based on demosaicing artifacts. In: ICIP(09), Cairo, Egypt
*Noise residual is found by calculating difference between original image and denosed image.
*J. Lukas, J. Fridrich, and M. Goljan, "Digital Camera Identification from Sensor Noise," IEEE Transactions on
Information Security and Forensics, vol. 1, no. 2, pp. 205-214, 2006.
•Rudin, L., Osher, S., and Fatemi, E.: Nonlinear total variation based noise removal algorithms, physica D., 60
(1992), 259-268.
CFA patterns we considered
(a)

(c)
(b)
(d)
(a1)
(a2)
(c1)
(c2)
(c3)
(d1)
(d2)
(d3)
(b1)
(b2)
(c4)
(c5)
(c6)
(d4)
(d5)
(d6)
(a3)
(a4)
(a5)
(a6)
(b3)
(b4)
(b5)
(b6)
(c7)
(c8)
(c9)
(d7)
(d8)
(d9)
(c10)
(c11)
(c12)
(d10)
(d11)
(d12)
Fig. 4. (a) Bayer Pattern [15]; (b) modified Bayer Pattern [15]; (c) Diagonal Strip Pattern [15];
(d) Vertical Striped Pattern [15].
[15] R. Lukac and K. N. Plataniotis: Color filter arrays: Design and performance analysis.
IEEE Transactions on Consumer Electronics, vol. 51, pp. 1260–1267 (2005)
What contributes to the
classification ability of this feature?

Sensors of different camera models may have different CFA
patterns.

Interpolation algorithm varies among different camera models.

Although some of the CFA patterns are not common in digital
cameras, grouping pixels periodically and find the statistical
difference could be able to capture artifacts during the whole
image processing pipeline.
Camera Model Detection——Feature
set 1: Design (1/2)

33 cases are designed


CFA types: Bayer, modified Bayer, diagonal strip, vertical
striped pattern
Color channel: Green, Red
r
e
var( N p i ) var( N p i )
m ax(
,
), i  1, ..., 33
e
r
var( N p i ) var( N p i )
(2)

and
denote raw and estimated noise under the ith
possible sample array

Total 36 cases, but 4 cases are same situation for our statistics
(Green channel under Bayer and modified Bayer pattern), Final
33 cases
Camera Model Detection——
Feature set 2

Inter- color channel interpolation
 A popular color difference interpolation scheme [19] utilizes
inter-channel correlation between colors to do interpolation. If we
assume that the full G is available by some interpolation process,
R can be recovered by equation (3). It also can be written as
equation (4).
R  LPF { R s  G s }  G (3)

Feature design

The difference of inter-color is ‘smoothed’, to capture these
artifacts, feature 2 is designed as equation (5-7)
var( N

R  G  LPF { R s  G s } (4)
R
G
 N ) (5)
var( N
B
G
 N ) (6)
var( N
R
B
 N ) (7)
[19] John S. Ho, Oscar C. Au, Jiantao Zhou, and Yuanfang Guo: INTER-CHANNEL
DEMOSAICKING TRACES FOR DIGITAL IMAGE FORENSICS. ICME2010
Camera Model Detection—— Feature
set 3

Design

For better identical issue, based on the first feature set, we
replace the variance statistics by kurtosis statistics and get
33 features as our third feature set. For the 33 possible
sample arrays, kurtosis can reflect minor difference of
interpolation. Hence, feature set 3 can be calculated as
equation (8).
r
e
kurtosis ( N p i ) kurtosis ( N p i )
m ax(
,
)
e
r
kurtosis ( N p i ) kurtosis ( N p i )
i  1, ..., 33
(8)
Camera Model Detection——
Feature Extraction Flow Diagram
Image
De-noising filter
Noise image
33 possible sample arrays
Case 1
Feature set 2 extraction
f12
f22
Case 2
…...
Case 33
N p1 r N p2 r
N p33 r
…...
e
N p1 e N p2
N p33 e
Feature set 3 extraction
…...
Feature set 1 extraction
f32
f11
f 21
…...
f13
f 23
f 331
69-D proposed features
Fig. 5. feature extraction flow diagram
…...
f 333
Experiment: Database (1/5)

‘Dresden Image Database’


Setting


a public database designed for benchmarking algorithms in
the area of digital image forensics.
Most under same or similar acquisition procedure, such as at
the same scenes, same taken positions, and same up to two
motives with tripods in Dresden, and photographed with each
camera of one set with systematically varying camera setting
(flash, focal length and interchanging lens, if possible) [21].
For source device detection

A collection of same or similar scene images taken by
different camera, which can be categorized to do
manufactory, model, or device detection or etc.
Experiment: Database (2/5)

Table.1. Camera model
No.
model
Device
num/model
Image
num/model
Image
resolution
Image
format
1
CanonIxus70
3
567
3072×2304
JPEG
2
CasioEXZ150
5
925
3264×2448
JPEG
3
FujiFirmFinePixJ50
3
630
3264×2448
JPEG
4
NikonCoolPixS710
5
925
4352×3264
JPEG
5
NikonD70s
2
367
3008×2000
JPEG
6
NikonD200
2
752
3872×2592
JPEG
7
KodakM1063
5
2391
3664×2748
JPEG
Experiment: Method (3/5)

Image Blocking


Feature extraction


four 512x512 sub-blocks from center of each JPEG image (no
compression after blocking)
69-D features are extracted from each sub-block
Model classification

1
2
3
4
Fig. 6. blocking position
90% of the images are randomly chosen for training the classifier
and the rest of them are used for testing (The random choosing
is controlled to make sure the sub-blocks in training part and
testing part are not from same image).
Experiment: Result (4/5)

Table.2. camera model detection accuracy
1
CanonIxus70
2
3
99.65
4
5
99.88
*
0.11
FujiFirmFinePixJ50
1.43
98.39
0.12
0.22
NikonD70s
NikonD200
KodakM1063
99.76
0.07
*
7
0.35
CasioEXZ150
NikonCoolPixS710
6
0.06
*
0.31
*
98.98
0.58
*
*
1.23
98.70
0.07
*
*
*
• Average detection rate under 20 times
*
99.87
Experiment: Result (5/5)

The average detection accuracy of our proposed method is 99.32%, For
324-D Markov feature [20], the average detection accuracy is 98.78%.
1
0.9
accuracy of detection
0.8
69D Proposed
324D Markov
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
model1 model2 model3 model4 model5 model6 model7
Fig. 7. average detection accuracy
[20] Guanshuo Xu, Shang Gao, Yun Qing Shi, RuiMin Hu and Wei Su: Camera-model
identification using Markovian transition probability matrix. (IWDW09)
Conclusion

The artifacts introduced by CFA and interpolation can
be considered as differences between models. Three
feature sets are designed to catch the artifacts.
Combining them together, 69-D features are obtained to
do model detection.

We use images from seven models of the Dresden
image database as our sample resource. The
experiment result shows that the detection accuracy of
our proposed method works well on seven camera
models. The average detection accuracy is 99.32%.
Qestions?