Post on 01-Jun-2018
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Forgery Authentication Using Distortion Cue
and Fake Saliency Map
Abstract
The widespread availability of photo manipulation software has made it
unprecedentedly easy to manipulate images for malicious purposes. Image
splicing is one such form of tampering. In recent years, researchers have
proposed various methods for detecting such splicing. In this paper, we
present a novel method of detecting splicing in images, using discrepancies
in motion blur. We use motion blur estimation through image gradients in
order to detect inconsistencies between the spliced region and the rest of
the image. We also develop a new measure to assist in inconsistent region
segmentation in images that contain small amounts of motion blur.
Experimental results show that our technique provides good segmentation of
regions with inconsistent motion blur. We also provide quantitative
comparisons with other existing blur-based techniques over a database of
images. It is seen that our technique gives signicantly better detection
results.
Distortion is often considered as an unfavorable factor in most image analysis. Hoever! it is
undeniable that the distortion reflects the intrinsic property of the lens! especially! the e"treme
ide#angle lens! hich has a significant distortion. $n this paper! e discuss ho e"plicitlyemploying the distortion cues can detect the forgery ob%ect in distortion image and make the
folloing contributions& '( a radial distortion pro%ection model is adopted to simplify the
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traditional captured ray#based models! here the straight orld line is pro%ected into a great
circle on the vieing sphere) *( to bottom#up cues based on distortion constraint are provided
to discriminate the authentication of the line in the image) +( a fake saliency map is used toma"imum fake detection density! and based on the fake saliency map! an energy function is
provided to achieve the pi"el#level forgery ob%ect via graph cut. ,"perimental results on
simulated data and real images demonstrate the performances of our method
Existing System
The power of the visual medium is compelling and so, malicious tampering
can have signicant impact on people!s perception of events. "isleading
images are used for introducing psychological bias, sensationali#ing news,
political propaganda, and propagating urban myths. The image in $ig. %,
ta&en from '%(, is an instance of the latter. This photograph was widely
circulated via e-mail, supposedly having been obtained from a camera found
in the debris of the World Trade )enter buildings after the attac&s of
*eptember %%, +%. The approaching aircraft in the bac&ground seems to
imply that this image was captured mere seconds before the impact.
owever, this image is clearly fa&e.
"any techniques have been developed to discover splicing and compositing
of images . *tatistical analyses and lighting inconsistencies. may be used in
order to detect image tampering. ther methods involve exploiting certain
features of images which are characteristic of the imaging systems, formats,
and the environment
Proposed System
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This paper is an extension of our wor& reported . We change the motion blur
estimation technique from spectral matting to image gradients for faster
processing. We rene the segmentation process in order to provide better
results and deal with cases of more complex blurs.We develop new measures
in order to reduce the amount of human intervention needed in our
technique and improve its robustness. We also provide a detailed
quantitative analysis of the e/ciency of our technique and test our
technique on a database of images.
e present a novel -inary artitioning /ree analysis ork for Semi#supervised image
segmentation! hich shos the suitability for the application of forged ob%ect e"traction. From
an over#segmentation result and the generated -/! the proposed -/ analysis algorithm
automatically selects an appropriate subset of nodes to represent a more meaningful
segmentation result. 0ne is that a novel dissimilarity measure considering the impact of color
difference! area factor and ad%acency degree in a unified ay is proposed for region merging and
used in the -/ generation process. /he other is the proposed -/ analysis algorithm! in hich
the node evaluation is designed to reasonably identify forged regions! and the folloing to#
phase node selecting process guarantees a meaningful segmentation result possibly reserving
forged regions. As an unsupervised image segmentation approach! our approach improves the
segmentation performance from the vie of forged ob%ect e"traction.
Modules
1. Image Preprocessing
/he image division operator normally takes to images as input and produces a third hose pi"el
values are %ust the pi"el values of the first image divided by the corresponding pi"el values of the
second image. Many implementations can also be used ith %ust a single input image! in hich
case every pi"el value in that image is divided by a specified constant. /he pi"el values are
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actually vectors rather than scalar values 1e.g.for color images( than the individual components
1e.g.red! blue and green components( are simply divided separately to produce the output value.
/he division operator may only implement integer division! or it may also be able to handle
floating point division. $f only integer division is performed! then results are typically rounded
don to the ne"t loest integer for output. /he ability to use images ith pi"el value types other
than simply 2#bit integers comes in very handy hen doing division.
2. Edge Classifcation
/he given image is represented in scale#space by repeatedly smoothing ith a 3aussian
filter of increasing si4e. '* levels in scale space are e"tracted. /hen! the features5
locations and scales are decided by using scale#adapted 6aplacian of 3aussian 16o3( !
hich can indicate gradient changes and corners in the image. For the 6o3 filter! the
output of the standard 6o3 filter is convolved ith a scale#adapted 3aussian filter!
hereas a similar approach is adopted for the 3uassian filter by using a multi#scale
3uassian operator. 6ikely candidates for feature points are selected based on having
certain values resulting from the 6o3 and Harris filters. For points having a Harris filter
response greater than a prespecified threshold! e sort the points in decreasing order of
6o3 filter response. /he 7 points ith the ma"imum responses 1sub%ect to meeting
e"clusion 4one criteria( are selected for signature e"traction. An e"clusion 4one of M
pi"els is used around each feature to maintain a minimum spatial distance beteen to
features. /his is important because it is 8uite likely that points shoing the highest
response to the filters tend to come from the same ob%ects or regions! and e ish to
avoid clustering of features in certain areas of the image.
Feature Extraction:
A circular region around each feature point 1ith a radius dependent on the scale( is
scaled to a radius of +* pi"els! alloing for robustness to scale changes. /his region is
then sub%ected to the trace transform ! hich is a generali4ation of the 9adon transform. $t
generali4es the 9adon transform by alloing for various functionals 1in addition to the
integral used in the 9adon transform( to be applied over straight lines in the circular
region. -y a proper choice of a functional! invariance to rotation and scaling can be
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achieved. /he trace transform is computed by draing samples 1inde"ed by t( along lines
parameteri4ed by distance
RDP Model Generation
$n the 9Dmodel! the straight orld line is first pro%ected to a great circle on the vieing sphere
and then pro%ected to a conic on the image plane. /his pro%ection! here the purple lines denote
the pro%ection of a straight orld lines . /he conic is fitted from the pro%ected points of purple
orld line. /he points represent to endpoints and one midpoint of the candidate line and are
back#pro%ected into points ! respectively. /he points define a great circle ! hich is pro%ected to
the straight line in the space ith the center of the vieing sphere. $n contrast! the points lie on
the forgery line! hich could be presented as a conic section on the forgery ob%ect. /he three
points are back#pro%ected to points on the vieing sphere! hich defines another circle . 7ote
this circle is not a great circle on the vieing sphere! because the forgery line is not satisfying
this geometric constraint any more. $n other ords! the circle generated by the forgery line
determines a plane section! hich cuts the vieing sphere ithout passing the center of the vie
sphere! as shon as the red dotted circle . /hanks to this constraint of line! there are to effective
bottom#up cues! the volume cue and the distance cue! hich could be used to distinguished the
forgery line.
Volume Cue Extraction
:ith the geometrical constraint! if three points on the image plane are pro%ected from the straight
orld line! their back#pro%ected points on the vieing sphere follo the geometrical constraint
here ;olume is the volume of tetrahedron made up by the center of vieing sphere and points
of the great circle ! as shon as the purple shado. Statistically! the forgery line is highly
unlikely to satisfy this geometrical constraint! as the blue shado. So the volume cue is defined
as a bottom#up cue here is the length of the forgery line in the image. the curves of the volume
cue of each candidate line ith different virtual focal length of the 9D model! here the
volume cues of original lines are close to 4ero and the cues of forgery lines are more likely to be
aay from the 4ero. -ut in the e"periments! the volume cue of the forgery line! hich locates
aay from the image center! is not distinguished clearly ith the cues of original lines! such as
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the right#most candidate line in the second sample. 7ote that ith the virtual focal length ! the
9D model degenerates and all the distortion values e8ual to 4ero! because the pro%ective
transformation of the Step * is invalidated and the incidence angle of every pi"el vanishes
identically .
Distance Cue Estimation
Same as the volume cue! the distance beteen the center and the plane! hich is defined by three
pro%ected points on the vieing sphere! is also considered as a cue. Suppose the matri" is
composed of a general point and three points on the vie sphere hich define the plane . Since
for points on e can read off the plane coefficients
FAKE SALIENCY MAP GENERATION
Segmentation of forgery ob%ects in an image remains a challenging problem! as the forged ob%ect
1or detected region( is usually noisy and incomplete. $n this section! e introduce a fake saliency
map to ma"imum fake detection density. Firstly! the untrustorthy likelihood is employed to
initiali4e seed. And then the fake saliency map is generated by the untrustorthy likelihood to
e"press the location of forgery ob%ect. Finally! the forgery ob%ect is segmented by minimi4ing theenergy function via graph cut! hich achieves a pi"el#level segmentation. 7ote that our fake
saliency map is not constrained to a specific choice of the forensic methods! and here e still
continue the distortion cues to describe this method.
SYSTEM SPECIFICTI!"#
$%&'%E#
Proceor : Intel Pentiu! "
RAM : # G$
Me!or% : #&' G$
Di(la% : Intel )*' E+ ,GA
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SOFT-ARE:
OS : Microo.t /P (ro.eional
Tool : MATLA$ 01'