Image Processing Reference
In-Depth Information
2.4 Detection based on correlation coefficient
To detect image р belonging to specific camera С it is possible to calculate correlation ρС
between residual and structural noise n=p-F(p) for the camera:
(
n
n
)(
P
P
)
.
corr
(
n
,
P
)
C
C
C
C
n
n
P
P
Now it is possible to define distribution ρС (q) for different images q made by the camera C
and distribution ρC (q ') for images q ' made not by the camera C. Based on Neumann-Pirson
approach and minimizing error rate the reached accuracy of classification made from 78 %
to 95 % on 320 images from 9 digital cameras.
C
C
2.5 Identification technique of digital recording devices based on correlation of digital
images
For development of a technique of identification of photocameras under images it is
necessary to consider architecture of prospective system of identification. The system
includes units:
- Input format converter;
- Detector of container modifying;
- Feature vector former;
- Feature vector saving;
- Feature vector search and extraction;
- Device identification.
An input format for identification system should be lossless format like full-color BMP to
which all images and video streams are convertible. Typical output formats of modern
cameras are JPEG and TIFF. In the feature vector former, digital image is converted to the
feature vector represents an image for identification an storage purposes.
In the unit of device identification the estimation of likeness of two or more vectors is
estimated allowing to accept or reject device similarity hypothesis.
2.5.1 Feature vector forming for digital cameras identification
Feature vector former is based on photosensitive matrix identification techniques, namely
PRNU-features. As there will always be both signal and noise (PRNU-components and
image context and (or) other noises) it is preferable to use filters to increase signal-noise
ratio. To select HF-components, which represent PRNU can be done by Wiener filtering:
1
an n
(, )
12
NM
nn
,
12
1
2
2
2
ann
(, )
12
NM
nn
,
12
2
2

bn n
(, )
 
((, )
an n
)
,
where N and M are number of pixels of neighborhood by y and x axis respectively.
12
12
12
2
(n ,n ) coordinates.
Thus averaged values for specific matrix is:
a( n , n ) — is a value of pixel with
12
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