Image Processing Reference
In-Depth Information
photocamera. The method also can be used for check on authenticity of pictures since in
those areas at which there are signs of editing the responses of neural network increase by 2-
3 times. However, usage of replaced fragments of the image from the same photocamera as
a background makes detection impossible [15]. Mehdi Kharrazi, etc. in [16] proposed the
method of photocameras identification on the basis of image features. The task of
determination of the camera with which help the analyzable picture has been received was
thus considered. Proceeding from known sequence of information processing from a
photosensitive matrix, it is possible to select two stages, importing the most essential
distortions: a stage of debayerization, i.e. full-color image restoration and a postprocessing
stage. Totally authors select 34 signs of classification, among them:
-
Cross-channel correlation R-G, R-B, B-G (3 scalar features).
-
Center of mass for histograms of differences number of pixels with i , i  and i
values (3 scalar features).
-
Channelwise power channel wise ratio of color components:
2
2
2
G
G
B
E
,
E
,
E
.
1
2
1
2
1
2
B
R
R
- statistics of wavelet transform (subspace decomposition by quadrature mirror filters
and averaging each sub-band) (9 features).
Along with enumerated features metrics of image quality proposed in [16] has been used.
All used metrics can be divided into following groups:
- pixelwise difference metrics (MSE, AMSE).
- correlation metrics (for example normalized mutual correlation).
- spectral difference metrics.
To classify vectors the SVM-based classifier has been used. At learning stage 120 of 300
images were used, with 180 at test stage. An average accuracy of camera identification in “1
out of 2” were 98,73% with 88,02% when images were regular photos.
In [17] an identification method based on proprietary interpolation algorithms used in
camera. The basis of algorithm is pixel correlation estimation listed in [18] with two
estimations: estimation of pixel value by adjacent pixels' values and demosaic kernel used
for raw data processing. As precise configuration of area used for interpolation is uknown,
several different configurations were used, with additional assumbtion about different
interpolation algorithms used in gradient and texturized areas. Camera identification
experiments were done on a basis of two cameras: Sony DSC-P51 и Nikon E-2100. It has
been acknowledged that filter kernel increase leads to accuracy increase (for kernels 3x3,
4x4, 5x5, accuracies were from 89.3 to 95.7%).
2.2 Camera identification based on matrix defects
Camera identification based on postprocessing algorithms features posess several
disadvantages, the most fundamental is impossibility of practical use for one-model camera
identification, even in “1 ot of 2” case.
In [19] camera identification method based on defective (“hot” and “dead” pixels) are
presented but its effectiveness is limited for cameras without build-in pixel defects
correction and “dark frame” subtraction.
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