Image Processing Reference
In-Depth Information
(
IFI
( ))
,
W
N
apprx
N
where F( I ) is a filter operation.
The best results were achieved by 5  mask. It has been shown that Wiener filter provides
better separation, comparing with wavelet transform filter in [21]. The offered identification
technique has been researched for identification possibility of 13 cameras [22-27], each with
100 images. Images from every camera were divided into 2 sets - training set used for
camera fingerprinting and the test set, used for identity check [21]. The central crop of an
image with 1024x1024 pixels size was used for identification purposes.
To process an image I for fingerprint creation or identification the color-to-grayscale
conversion has been applied. Fingerprint is an averaged sum of all HF-components, forming
W
value. To check identity of an image
I
, the correlation coefficient is evaluated:
apprx
q
p cFI ,
where p — is a correlation coefficient, and cc — cross correlation.
(( ,
)
q
apprx
1 2 3 4 5 6 7 8 9 10 11 12 13
1 0.0908 0.0010 0.0042 0.0004 0.0037 0.0026 0.0028 0.0020 0.0040 0.0033 0.0031 0.0032 0.0035
2 0.0006 0.1494 -0.0001 0.0015 0.0005 0.0006 -0.0001 0.0000 0.0005 0.0003 0.0004 0.0001 0.0007
3 0.0028 -0.0001 0.1364 0.0003 0.0018 0.0004 0.0017 0.0020 0.0030 0.0023 0.0017 0.0024 0.0016
4 -0.0001 0.0009 -0.0004 0.1889 0.0002 -0.0007 -0.0004 -0.0009 -0.0000 -0.0003 -0.0007 -0.0001 -0.0005
5 0.0054 0.0019 0.0035 0.0000 0.0727 0.0025 0.0042 0.0024 0.0044 0.0022 0.0033 0.0029 0.0038
6 0.0022 0.0004 0.0004 0.0000 0.0015 0.1423 0.0006 0.0001 0.0016 0.0009 0.0013 0.0007 0.0036
7 0.0010 -0.0007 0.0005 -0.0001 0.0010 0.0001 0.2645 -0.0012 0.0010 0.0008 0.0012 0.0017 0.0014
8 0.0003 0.0008 0.0009 -0.0001 0.0004 -0.0001 -0.0005 0.7079 0.0000 0.0012 -0.0001 0.0009 -0.0005
9 0.0049 0.0004 0.0046 0.0001 0.0031 0.0018 0.0027 0.0029 0.1038 0.0017 0.0033 0.0036 0.0019
10 0.0027 0.0013 0.0031 0.0002 0.0023 0.0015 0.0017 0.0032 0.0025 0.1005 0.0090 0.0030 0.0014
11 0.0011 0.0007 0.0020 -0.0010 0.0013 0.0004 0.0012 -0.0002 0.0006 0.0013 0.3776 0.0006 0.0007
12 0.0025 0.0002 0.0023 -0.0006 0.0018 0.0004 0.0023 0.0016 0.0028 0.0016 0.0020 0.1294 0.0016
13 0.0015 0.0001 0.0010 -0.0006 0.0017 0.0022 0.0009 -0.0006 0.0008 0.0009 0.0009 0.0003 0.2747
Table 1. An averaged correlation coefficients for 13 cameras.
On intersection of columns and lines with identical indexes there are correlation coefficients
of images and a fingerprint, received by the same camera. Thus, at matrix coincidence,
correlation value is 0.1 - 0.7 and for incoincident cameras is 0.001 - 0.054.
2.6 Image rotation detection based on Radon transform
Photosensitive matrix of a modern digital camera naturally possesses non-uniformity of its
elements, both photosensitive and signal amplifiers. As the charge is transferred by
columns, the well-known phenomena called banding occurs, resulting high-frequency noise.
After image reconstruction process [3] and subjective quality improvements completing, the
resulted image is compressed, usually according to JPEG standard, which introduces
blocking effect, and contributes regular pattern to rows and columns as well.
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