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FORENSIC TECHNIQUES FOR CLASSIFYING SCANNER, COMPUTER GENERATED
AND DIGITAL CAMERA IMAGES
Nitin Khanna†, George T.-C. Chiu‡, Jan P. Allebach†, Edward J. Delp†
†School of Electrical and Computer Engineering
‡School of Mechanical Engineering
Purdue University
West Lafayette, Indiana
ABSTRACT
Digital images can be captured or generated by a variety of
sources including digital cameras, scanners and computer
graphics softwares. In many cases it is important to be able
to determine the source of a digital image such as for crimi-
nal and forensic investigation. This paper presents methods
for distinguishing between an image captured using a digital
camera, a computer generated image and an image captured
using a scanner. The method proposed here is based on the
differences in the image generation processes used in these
devices and is independent of the image content. The method
is based on using features of the residual pattern noise that
exist in images obtained from digital cameras and scanners.
The residual noise present in computer generated images
does not have structures similar to the pattern noise of cam-
eras and scanners. The experiments show that a feature based
approach using an SVM classifier gives high accuracy.
Index Terms—image forensics, digital camera, scanners,
computer graphics, pattern noise.
1. INTRODUCTION
Advances in digital imaging technologies have led to the de-
velopment of low-cost and high-resolution digital cameras
and scanners. Digital images produced by various sources are
widely used in a number of applications from medical imag-
ing and law enforcement to banking and daily consumer use.
There is also proliferation of software for generating as well
as manipulating digital images. Forensic tools that help estab-
lish the origin, authenticity, and the chain of custody of digital
images are essential for many applications [1].
There are various levels at which the image source iden-
tification problem can be addressed. One may want to find
1This material is based upon work supported by the National Sci-
ence Foundation under Grant No. CNS-0524540. Any opinions, find-
ings, and conclusions or recommendations expressed in this material are
those of the author(s) and do not necessarily reflect the views of the Na-
tional Science Foundation. Address all correspondence to E. J. Delp at
ace@ecn.purdue.edu.
the particular device (digital camera or scanner) which gener-
ated the image or one might be interested in knowing only the
make and model of the device. A number of robust methods
have been proposed for source camera identification [2, 3, 4,
5, 1].
Poineering work in utilizing imaging sensor’s pattern
noise for source camera identification is presented in [3]. The
identification is based on pixel nonuniformity noise for both
CCD (Charged Coupled Device) and CMOS (Complemen-
tary Metal Oxide Semiconductor) sensors. The pattern noise
is caused by several factors such as pixel non-uniformity,
dust specks on the optics, optical interference, and dark
currents [6]. The high frequency part of the pattern noise is
estimated by subtracting a denoised version of the image from
the original using a wavelet denoising filter [3]. A camera’s
reference pattern, estimated by averaging the noise patterns
from multiple images, serves as an intrinsic signature of the
camera. To identify the source camera, the noise pattern from
an image is correlated with known reference patterns from a
set of cameras [3].
There are similar approaches for source scanner identifi-
cation using sensor noise. In [7], a direct extension of the
camera identification algorithm [3] was used for source scan-
ner identification. All the experiments have shown lower clas-
sification accuracy compared to similar methods for source
camera identification. Further experiments show that one pos-
sible reason for the decline in performance is post-processing
operations in the scanners such as denoising techniques in-
cluding flat-fielding and heavy down-sampling [7].
Another approach for scanner model identification using
sensor pattern noise described in [8] uses three sets of features
for each scanned image. Experiments on a set of 26 images
from 7 scanners give 90-96% average classification accuracy.
In [9] pattern noise based source camera identification [3] was
extended for scanner identification using a set of statistical
features and a SVM classifier. Experiments show that 95%
average classification accuarcy is possible when scanning is
done at native resolution of the scanner.
The techniques used for both source camera and scanner
identification are dependent upon having prior knowledge of
the class of device (camera or scanner). If the image was gen-
erated by a digital camera, then the digital camera identifica-
tion methods must be used. Similarly if the image was gener-
ated by a scanner, the scanner identification methods must be
used.
In [10] a method for differentiating between computer
generated (henceforth refered as CG) and photographic im-
ages based on wavelet statistics is presented. It has been
shown that a model based on first and higher-order wavelet
statistics reveals subtle but significant differences between
CG images and photographic images. Motivated by the use
of the pattern noise introduced during image acquisition as a
unique characteristic of digital cameras [3], in [11] a method
for distinguishing between digital camera images and com-
puter generated images is proposed. This method is based
on the observation that since the image sensor technology
remains the same even if each individual camera has a unique
noise pattern associated with it, pattern noise introduced by
different digital cameras may have common properties and
this common characteristic will not be present in computer
generated images. This scheme can not be easily extended to
include scanner generated images due to the registration prob-
lems in generating the scanner error reference pattern [7, 9].
In [12], a novel technique for classification of images
based on their sources, scanned and non-scanned images, is
presented. A SVM classifier is used with appropriate features
of the sensor pattern noise. For distinguishing images scanned
at native resolution of the scanner from those captured using
a digital camera, an average classification accuracy of greater
than 95% is obtained.
In this paper we will extend the above feature vector
based method [12] for classifying images captured using dig-
ital cameras, computer generated images and images captured
using scanners.
2. FEATURE EXTRACTION
Both digital cameras and scanners work on similar principles
in terms of their imaging pipeline. However, digital cameras
use a two dimensional sensor array while most scanners use
a one dimensional linear array. In the case of flatbed scan-
ners, the same linear array is translated to generate the en-
tire image. It is expected to find periodic correlation between
rows of the fixed component of the sensor noise of a scanned
image. There is no reason to find a similar periodic correla-
tion between columns of the sensor noise of a scanned image.
Neither the rows nor the columns of the fixed component of
the sensor noise of an image generated by a digital camera
are expected to exhibit such periodicity. This difference can
be used as a basis for discriminating between the two image
source classes. Further, due to the fundamental differences in
the image generation process, the residual noise in computer
generated images may not have properties similar to those of
images from the other two classes. Thus, for distinguishing
images we develope an approach similar to that of [12] with
suitable modifications in the features.
Let Idenote the input image of size M×Npixels (M
rows and Ncolumns) and Inoise be the noise corresponding
to the image. Let Idenoised be the result of applying the de-
noising filter [13] on I. Then as in [3],
Inoise =I−Idenoised (1)
To save computation time, only the green channel is used
for denoising and feature extraction. Let e
Ir
noise and e
Ic
noise
denote the average of all the rows and the columns of the noise
(Inoise) (Equation 2, 3).
e
Ir
noise(1, j ) = 1
M
M
X
i=1
Inoise(i, j ); 1 ≤j≤N(2)
e
Ic
noise(i, 1) = 1
N
N
X
j=1
Inoise(i, j ); 1 ≤i≤M(3)
Further, let ρrow (i)denote the value of correlation be-
tween the average of all the rows (e
Ir
noise) and the ith row
of the noise (Inoise) (Equation 4). (Similarly ρcol (j), Equa-
tion 5 ).
ρrow (i) = C(e
Ir
noise, Inoise (i, .)) (4)
ρcol(j) = C(e
Ic
noise, Inoise (., j)) (5)
Where C(X, Y )is the normalized correlation between
two vectors Xand Y. For scanned images, ρrow is expected
to have higher values than ρcol since there is a periodicity be-
tween rows of the fixed component of the sensor noise of a
scanned image. The mean, standard deviation, skewness and
kurtosis of ρrow and ρcol are the first eight features, extracted
from each input image. The standard deviation, skewness and
kurtosis of e
Ir
noise and e
Ic
noise correspond to features 9through
14. The last feature for every input image is given by the
following:
f15 = 1−
1
NPN
j=1 ρcol(j)
1
MPM
i=1 ρrow (i)!∗100 (6)
A total of 15 features is obtained for each image. These
features capture the essential properties of the image which
are useful for discriminating between different image sources.
Note that for extracting these statistical features, we need not
to know the scan direction, that is, whether the image was
scanned as portrait or as landscape. This is because the aver-
age of ρrow is always higher than the average of ρcol and so
if needed, we can just rotate the image (rotating noise will be
sufficient) before estimating the feature vector.
Table 1. Image Sources Used in Experiments
Image class Devices used
Digital Camera Canon PowerShot SD200, Nikon Coolpix 4100, Nikon Coolpix 7600
Computer Generated www.3dlinks.com, www.irtc.org, www.raph.com, www.digitalrepose.com, www.maxon.net, www.realsoft.com
Flatbed Scanners Epson Perfection 4490 Photo, HP ScanJet 6300c-1, HP ScanJet 6300c-2, HP ScanJet 8250, Mustek 1200 III EP,
Visioneer OneTouch 7300, Canon LiDe 25, Canon Lide 70, OpticSlim 2420, Visioneer OneTouch 7100, Mustek ScanExpress A3
3. EXPERIMENTS AND RESULTS
Table 1 shows the sources of different classes of digital im-
ages used in our experiments. Some of the scanners have
CCD sensor while others have CIS sensor. Computer gen-
erated images include images from number of different meth-
ods such as 3ds max, Maya, Softimage and Lightwave. From
each of the 11 scanners 108 images were scanned at 200dpi
resolution and stored in TIFF format (1024 ×768 pixels).
Approximately 300 images were captured from each of the
three cameras at 1024 ×768 resolution and stored in the best
quality JPEG format supported by each camera. Computer
generated images, in JPEG format, were downloaded from
publicaly available websites listed in Table 1. For computer
generated images of varying sizes, a central 1024 ×768 or
smaller block is used for feature extraction depending upon
the size of the image. In total there are approximately 1000
images from each of the three source classes. The LIBSVM
package [14] is used in this study for the SVM classifier. A
radial basis function is chosen as the kernel function and a
grid search is performed to select the best parameters for the
kernel. Unless stated otherwise, randomly chosen 80% of the
images are used for training the classifier and rest of the im-
ages are used for testing. This training and testing is repeated
multiple times to obtain the final average classification results.
Fig. 1. Image Source Classification
The experiment is shown in Figure 1. In first set of ex-
periments three separate SVM classifiers are designed for
distinguishing between three possible pairs of image source
classes: scanner, computer generated and camera. Tables 2, 3
and 4 show the confusion matrices for classifying these pair
of classes. The average classification accuracy for distin-
guishing Scanner images from CG images is 97.6%. The
average classification accuracy for distinguishing CG images
from Camera images is 91.5%. While the average classifica-
tion accuracy for distinguishing Camera images from Scanner
images is 89.4%, the lowest among three pairs.
Table 2. Confusion Matrix for Scanner vs. CG
Predicted
Scanner CG
Scanner 98.2 1.8
Actual CG 3.1 96.9
Table 3. Confusion Matrix for CG vs. Camera
Predicted
CG Camera
CG 88.3 11.6
Actual Camera 5.2 94.8
Table 4. Confusion Matrix for Camera vs. Scanner
Predicted
Camera Scanner
Camera 89.5 10.5
Actual Scanner 10.7 89.3
Table 5. Confusion Matrix for Scanner, CG and Camera
Predicted
Scanner CG Camera
Scanner 85.3 1.0 13.7
Actual CG 1.7 88.3 10.0
Camera 11.9 3.9 84.2
The average classification accuracy for classifying images
from all three classes is 85.9%. Corresponding confusion ma-
trix is shown in Table 5. Thus, by training an SVM classifier
on the 15 dimensional feature vectors from each image, using
randomly chosen 800 images from each class for training and
separate 200 images for testing, proposed method is able to
give average classification accuracy of 85.9%.
The efficacy of the proposed method is also tested on
images that have been JPEG compressed. An average classi-
fication accuracy of 79.8% is obtained when all the scanned
images are saved as JPEG (Q=90) before feature extraction
for classifier training and testing. Corresponding confusion
matrix is shown in Table 6. This slight decrease in per-
formance is as expected since the pattern noise degrades
with JPEG compression and down-sampling, as observed for
source camera identification [3] and source scanner identifi-
cation [7]. Whenever a camera or scanner is used to capture
images at resolution lower than maximum resolution sup-
ported by the device, generally down-sampling is done in the
device driver, for example scanning at 200dpi from a 4800dpi
scanner. Further experiments by varying the size of train-
ing dataset show that average classification accuracy remains
close to 80% even when only 40% images (400 images from
each source class) are used for training the classifier.
Table 6. Confusion Matrix for Classifying JPEG Compressed
Images Predicted
Scanner CG Camera
Scanner 86.4 1.7 11.9
Actual CG 11.8 70.6 17.6
Camera 13.4 4.2 82.4
4. CONCLUSION
In this paper we investigated the use of the sensor pattern
noise for classifying digital images based on their sources.
Selection of proper features is the key to achieve accurate re-
sults. The scheme presented here utilizes statistical properties
of the residual noise and the difference in the geometry of the
imaging sensors and demonstrates promising results. Future
work will include, tests on images that have undergone vari-
ous post-processing operations.
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