Tr 00049

IDL - International Digital Library Of Technology & Research Volume 1, Issue 4, Apr 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017

Image Forgery Detection Using Feature Point Matching and Adaptive Over Segmentation Seema Sultana, Sunanda Dixit Department of Information Sciences, Dayananda Sagar College of Engineering, Bangalore, India Seemasultna17@gmil.com

fabrication

applies the morphological operation to the

location conspire utilizing highlight point

combined locales to create the identified

coordinating and versatile over-division is

fabrication areas. The trial comes about show

worked here. This plan coordinates both

that the proposed duplicate move falsification

square based and key point-based falsification

identification plan can accomplish much better

location strategies. To start with, the proposed

location comes about even under different

Adaptive

calculation

testing conditions contrasted and the current

fragments the host picture into non-covering

cutting edge duplicate move fabrication

and sporadic pieces adaptively. At that point,

discovery strategies.

the component focuses are removed from each

Key words: Adaptive-Over Segmentation,

piece as square elements, and the square

Forgery Region Extraction, Fabrication areas

Abstract: A

duplicate

move

Over-Segmentation

elements are coordinated with each other to

1 Introduction

find the named include focuses, this strategy can around show the presumed fraud districts.

The

To recognize the phony locales all the more

computerized control to even now and moving

precisely, the Forgery Region Extraction

pictures

calculation is exhibited, which replaces the

misdirection,

component focuses with little superpixels as

uprightness. With experts testing the moral

highlight pieces. At that point combines the

limits of truth, it makes a potential loss of

neighboring hinders that have comparable

open trust in computerized media [1]. This

nearby shading highlights into the component

spurs the requirement for location devices that

squares to create the blended areas. At last, it

are straightforward to altering and can tell

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presentation

raises

and

moral and

quick

spread

issues

of

advanced

of

truth, picture

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 4, Apr 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017 whether a picture has been altered just by

of data, and the dependability of computerized

investigating the altered picture. Picture

pictures is along these lines turning into an

altering is a computerized craftsmanship

essential issue [4]. As of late, an ever

which

picture

increasing number of scientists have started to

properties and great visual imagination [2].

concentrate on the issue of computerized

One alters pictures for different reasons either

picture altering. Of the current sorts of picture

to appreciate fun of computerized works

altering, a typical control of an advanced

making mind boggling photographs or to

picture is duplicate move falsification, which

deliver false confirmation. Regardless of

is to glue one or a few replicated region(s) of a

whatever the reason for act may be, the

picture into different part(s) of a similar

falsifier ought to utilize a solitary or a blend

picture.

needs

comprehension

of

arrangement of picture handling operations.

Copy-Move Operation

Image Segmentation

Amid the duplicate and move operations, some

Division is the way toward apportioning an

picture preparing techniques, for example,

advanced picture into different portions (sets

revolution, scaling, obscuring, pressure, and

of pixels) [3]. The objective of division is to

clamor expansion are once in a while

streamline

the

connected to make persuading falsifications.

portrayal of a picture into something that is

Since the duplicate and move parts are

more important and simpler to break down.

replicated

Picture division is normally used to find

commotion segment, shading character and

articles and limits (lines, bends, and so on.) in

other essential properties are perfect with the

pictures. All the more correctly, picture

rest of the picture; a portion of the imitation

division is the way toward doling out a mark

recognition strategies that depend on the

to each pixel in a picture to such an extent that

related picture properties are not pertinent for

pixels with a similar name share certain visual

this situation. In earlier years, numerous

qualities.

fabrication location techniques have been

and

additionally

change

proposed

Image Forgery Detection

from

for

a

similar

duplicate

picture,

move

the

imitation

identification.

Computerized

picture

fraud

has

been

progressively simple to perform, because of

2 Detection Methods

the advancement of PC innovation and picture

The copy-move forgery detection methods fall

preparing

under two main categories:

programming.

Nonetheless,

advanced pictures are a well-known wellspring IDL - International Digital Library

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 4, Apr 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017 

Block-based algorithms

strategies, it is a picture blocking strategy



Feature keypoint-based algorithms.

brought

the

Adaptive Over-Segmentation

calculation to separate the host picture into

Block-Based Methods:

non-covering and sporadic pieces adaptively.

In the existing block-based methods, the image

At that point, like the key point-based phony

is segmented into overlapping and regular

recognition techniques, the element focuses

image blocks. Then the forgery regions are

are extricated from each picture hinder as

identified by matching blocks of image pixels

square elements as opposed to being removed

or transform coefficients. Some of the bloc-

from the entire host picture as in the

based methods are PCA, DCT, DWT, and

conventional key point-base strategies [7].

SVD [5].

Key-point based algorithms: An alternative to the block-based methods, keypoint-based forgery detection methods are proposed, where image keypoints are extracted and matched over the whole image to resist some image transformations while identifying duplicated regions.

Scale-Invariant Feature Transform (SIFT) is applied to host images to extract feature points, which are then match to one another.

When the value of the shift vector exceeded

Fig 1: Framework for copy-move forgery detection

the threshold, the sets of corresponding SIFT

scheme

feature points are defined as the forgery

The Adaptive Over-Segmentation calculation,

region. Speeded Up Robust Features (SURF)

which is like when the extent of the host

is also applied to extract image feature instead

pictures builds, the coordinating calculation of

of SIFT [6].

the covering pieces will be considerably more

3 Proposed System

costly. To address these issues, the Adaptive Over-division technique was proposed, which

This plan incorporates both the conventional

can portion the host picture into non-covering

square based phony location strategies and key

areas of sporadic shape as picture pieces a

point-based imitation recognition techniques.

short time later, the imitation locales can be

Like

square

based

phony

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 4, Apr 2017

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International e-Journal For Technology And Research-2017 recognized

non-

After the host picture is sectioned into picture

unpredictabledistricts.

squares, piece components are separated from

Divisiontechnique, the non-covering division

the picture squares (IB). The conventional

can

piece

covering

by and

diminish

contrasted

coordinating

and

the

those

computational

the

covering

costs

blocking;

based

techniques

falsification

extricated

identification

elements

of

an

moreover, as a rule, the sporadic and

indistinguishable length from the square

significant areas can speak to the fraud district

elements or specifically utilized the pixels of

superior to the consistent squares.

the picture obstruct as the square components.

Notwithstanding, these components reflect mostly the substance of the picture pieces, forgetting the area data. Additionally, these components are not impervious to different picture changes. Along these lines, in this venture, the component focuses are extricated from each picture hinder as square elements and the element focuses ought to be powerful to different twists, for example, picture

Fig 2: Flowchart of Adaptive-Over-Segmentation

scaling, revolution, and JPEG pressure.

4 Results In addition to the plain copy-move forgery, have tested proposed scheme when the copied regions are distorted by various attacks [8]. In this case, the forged images are generated by using each of the 48 images in the dataset, and the copied regions are attacked by geometric distortions that include scaling and rotation and common signal processing such as JPEG compression.

Fig 3: Flowchart of Block Feature Matching Algorithm

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 4, Apr 2017

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International e-Journal For Technology And Research-2017

Fig 5: SIFT Feature Detected Points

Fig 4: Input Image

Fig 6: SURF Feature Detected Points

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 4, Apr 2017

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International e-Journal For Technology And Research-2017 Fig 7: Matched Points

Future work could focus on applying the proposed forgery detection scheme based on adaptive over-segmentation and feature-point matching on other types of forgery, such as splicing or other types of media, for example, video and audio.

References [1] J. Fridrich, D. Soukal, and J. Lukáš, “Detection of copy–move forgery in digital images,” in Proc.

Digit.

Forensic Res.

Workshop, Cleveland, OH, Aug. 2003.

[2] X. B. Kang and S. M. Wei, “Identifying tampered

regions

using

singular

value

decomposition in digital image forensics,” in Proc. Int. pp. 926–930, Dec. 2008.

Fig 8: Forgery Detected

[3] S. Bayram, H. T. Sencar,“An efficient and

5 Conclusion

robust method for copy–move forgery,” in

Computerized falsification pictures made with duplicate move operations are trying to

Proc. IEEE Int. Conf. Acoust., Speech, Signal Process (ICASSP), pp. 1053–1056, Apr. 2009.

identify. A novel duplicate move phony

[4] S. J. Ryu, M. J. Lee, and H. K. Lee,

recognition conspire utilizing versatile over-

“Detection of copy-rotate-move forgery using

division and highlight point coordinating is

Zernike moments,” in Information Hiding.

proposed. The Adaptive Over-Segmentation

Berlin, Germany: Springer-Verlag, pp. 51–65,

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2010.

picture into non-covering and sporadic squares

[5] S. Bravo-Solorio and A. K. Nandi,

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discovery results can be resolved and, in the

1883, 2011.

meantime, lessen the computational costs.

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 4, Apr 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017 [6] X. Y. Pan and S. Lyu, "Region Duplication

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[8] B. Shiva Kumar and L. D. S. S. Baboo, "Detection of region duplication forgery in

[7] P. Kakar and N. Sudha, "Exposing Post

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