A New Copy Move Forgery Detection Technique using Adaptive Over-segementation and Feature Point Matching

Bulletin of Electrical Engineering and Informatics
Vol. 7, No. 3, September 2018, pp. 345~349
ISSN: 2302-9285, DOI: 10.11591/eei.v7i3.754  345
Journal homepage: http://journal.portalgaruda.org/index.php/EEI/index
A New Copy Move Forgery Detection Technique Using
Adaptive Over-segementation and Feature Point Matching
Anil Gupta
MIET, Kot Bhalwal, Jammu, India
Article Info ABSTRACT
Article history:
Received Sep 09, 2017
Revised May 25, 2018
Accepted Jun 08, 2018
With the development of Image processing editing tools and software, an
image can be easily manipulated. The image manipulation detection is vital
for the reason that an image can be used as legal evidence, in the field of
forensics investigations, and also in numerous various other fields. The
image forgery detection based on pixels aims to validate the digital image
authenticity with no aforementioned information of the main image. There
are several means intended for tampering a digital image, for example, copy-
move or splicing, resampling a digital image (stretch, rotate, resize), removal
as well as the addition of an object from your image. Copy move image
forgery detection is utilized to figure out the replicated regions as well as the
pasted parts, however forgery detection may possibly vary dependant on
whether or not there is virtually any post-processing on the replicated part
before inserting the item completely to another party. Typically, forgers
utilize many operations like rotation, filtering, JPEG compression, resizing as
well as the addition of noise to the main image before pasting, that make this
thing challenging to recognize the copy move image forgery. Hence, forgery
detector needs to be robust to any or all manipulations and also the latest
editing software tools. This research paper illustrates recent issues in the
techniques of forgery detection and proposes a advanced copy–move forgery
detection scheme using adaptive over-segmentation and feature point
matching. The proposed scheme integrates both block-based and key point-
based forgery detection methods.
Keywords:
Copy-move image forgery
Image forgery
Image forgery detection
Tampering
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Anil Gupta,
Department of Information Technology,
Model Institute of Engineering and Technology, Kot Balwal, Jammu, India
Email: anil.it@mietjammu.in
1. INTRODUCTION
The present day digital revolution has changed the format of accessing, manipulating and sharing
information, however these developments have also given rise to different security issues. Complex digital
technology and various photo-editing software like Adobe Photoshop etc. are universal and have made the
task of forging images a common practice. [1]
Complex digital technology and different photo-editing software, such as Adobe Photoshop, are
universal and thus have made the process of manipulating images to create forgeries a fairly common
practice. As a result, trust in digital imagery has been eroded.
An example of digital forgery was seen in a Tunisian newspaper in which a photo was altered
duplicating the crowd to appear large. Another example as shown in Figure 1 displays the altered photograph
released by Iran showing four missiles instead of three [2]. This tampered image was also being published by
various western media including The New York Times. The research in this paper attempts to address this
need and provide some insight into this challenging problem.

 ISSN: 2302-9285
BEEI, Vol. 7, No. 3, September 2018 : 345 – 349
346
(a) Original image (b) Forged image
Figure 1. Example of copy-move forgery
2. IMAGE TAMPERING
Image tampering is defined as “adding or removing important features from an image without
leaving any obvious traces of tampering” and thus image tampering is considered as intentional manipulation
of images for malicious purposes [3]. There are various techniques for counterfeiting images and these can be
classified into three broad categories.
Copy-Move attack, also called Cloning, is a technique in which instead of having an external image
as the source, it uses portion of the original base image as its source. Therefore, the source and the destination
of the modified image originate from the same image. Photoshop Clone Stamp Tool can be used to achieve
such type of forgery. Blurring is usually applied along the border of the modified region to reduce the effect
of irregularities between the original and pasted region.
The second type of image tampering techniques is known as Image-Splicing, which is a technique
that involves a composite of two or more images which are combined to create a fake image. Thus, by
sticking together photographic images a spliced image is being obtained. [4]
And the third category of image tampering technique is known as Image-Retouching in which
certain features of image are being enhanced or reduced in order to make the image more attractive. Thus,
this type forgery is considered less harmful and is used mostly by the magazine editors. [5]
3. TECHNIQUES TO COUNTERATTACK FORGERY
To detect above mentioned digital forgeries in images two principle approaches are taken into
account namely, Active approach and Passive approach. [6]
In active approach, during the creation of images pre-processing techniques like watermark
embedding or signature generation are applied which limit the use of images in general. However, there are
millions of digital images on internet which are without any digital watermark or signature. In this context
active approach could not be used to find the authenticity of the image.
Therefore unlike the active approach, the passive approach does not need any embedded watermark
or digitally generated signature. Mainly three techniques are widely used to tamper digital images namely
Copy-Move, Splicing and Retouching as shown in Figure 2.

BEEI ISSN: 2302-9285 
A New Copy Move Forgery Detection Technique Using Adaptive Over-segementation and… (Anil Gupta)
347
Figure 2. Image forgery techniques and their solutions
4. COPY/MOVE FORGERY DETECTION
The copy-move forgery detection methods can be categorized into two main categories: block-based
algorithms and feature key point-based algorithms [7]. In both cases, preprocessing of the images is possible.
For instance, most methods operate on grayscale images, and as such require that the color channels be first
merged. For feature extraction, block-based methods subdivide the image in rectangular regions. For every
such region, a feature vector is computed. Similar feature vectors are subsequently matched. By contrast, key
point-based methods compute their features only on image regions with high entropy, without any image
subdivision. Similar features within an image are afterwards matched. A forgery shall be reported if regions
of such matches cluster into larger areas. Both, key point-based and block-based methods include further
filtering for removing spurious matches [8].
5. PROPOSED SYSTEM
We propose a new copy-move forgery detection scheme using adaptive over-segmentation and
feature point matching [9]. The proposed scheme integrates both the traditional block-based forgery detection
methods and key point-based forgery detection methods. Similar to block-based forgery detection methods,
we propose an image-blocking method called the Adaptive Over-Segmentation algorithm to divide the host
image into non-overlapping and irregular blocks adaptively. Then, similar to the keypoint-based forgery
detection methods, the feature points are extracted from each image block as block features instead of being
extracted from the whole host image as in the traditional key point-base methods. Subsequently, the block
features are matched with one another to locate the labeled feature points, which can approximately indicate
the suspected forgery regions. To detect more accurate forgery regions, we proposed the Forgery Region
Extraction algorithm, which replaces the feature points with small super pixels as feature blocks and, then,
merges the neighboring blocks with similar local color features into feature blocks, to generate the merged
regions; finally, it applies a morphological operation into the merged regions to generate the detected forgery
regions. Figure 3 shows the framework of the proposed image forgery detection scheme. First, an adaptive
over-segmentation method is proposed to segment the host image into non-overlapping and irregular blocks
called Image Blocks (IB). Then, we apply the Scale Invariant Feature Transform (SIFT) in each block to
extract the SIFT feature points as Block Features [10]. Subsequently, the block features are matched with one
another, and the feature points that are successfully matched to one another are determined to be Labeled
Feature Points, which can approximately indicate the suspected forgery regions. Finally, to detect the more
accurate forgery regions, we propose the Forgery Region Extraction algorithm, in which the labeled feature
points are replaced with small super pixels as feature blocks, and the neighboring feature blocks with local
color features that are similar to the feature blocks are merged to generate the merged regions. Table 1 shows
literature review.
Image
forgery
Active
Approach
Passive
Approach
Digital
Watermark
Tampering
Image
Slicing
Image
Retouching
CopyMove

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BEEI, Vol. 7, No. 3, September 2018 : 345 – 349
348
Figure 3. Framework of the proposed copy-move forgery detection scheme
Table 1. Comparision of Different Copy Move Image Forgery Techniques with the proposed method
Title Method Advantages Disadvantages
Detection of Copy-Move Forgery
in Digital Images [1]
Block based method. Image is
divided to blocks and forged
parts are detected with exact
match and appropriate match
It can detect images with
distortion format
Slow detection process
Exposing digital forgeries by
detecting duplicated image
regions [2]
Block based method. PCA
applied to obtain reduced
dimensional representation.
More reliable to detect noisy
and lossy images
Sometimes it failed to detect
difficult forgeries.
Robust Method for Detection
of Copy-Move Forgery in Digital
Images. [3]
Key-point based technique.
Wavelet transform technique is
used and computes phase
correlation to detect similarity.
Lower computational
complexity
Duplicated regions through
angles and scaled regions
cannot detect.
Segmentation-Based Image
Copy-Move Forgery Detection
Scheme [5]
Key-point based technique.
Extracted key points from
patches and matched for
duplicated regions.
Segment image into
semantically independent
patches
An accurate estimation of
transform matrix is obtained
by EM based algorithm [8]
Re-estimation of transform
matrix is complex.
Region Duplication Detection
Using Image Feature Matching
[4]
Key point based method. By
calculating SIFT key-points finds
pixels within the duplicated
regions.
Reliable than other key point
techniques
Sometimes it gives vague
results
A SIFT-Based Forensic Method
for Copy–Move Attack Detection
and Transformation Recovery [6]
Key point based method.
Key points are extracted and key
point localization is done for
detection.
Good at determine the
Geometric transformation.
Not good at detection phase
with respect to cloned image
patch with high uniform
texture
A New Copy Move Forgery
Detection Technique using
Adaptive over-Segementation
and Feature Point Matching
(Proposed Paper)
Adaptive over-segmentation and
feature point matching Method
Yields better results when
compared to the earlier
techniques.
Segments the host image into
non-overlapping and irregular
blocks adaptively.
No drawbacks

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A New Copy Move Forgery Detection Technique Using Adaptive Over-segementation and… (Anil Gupta)
349
6. CONCLUSION
Digital forgery images created with copy-move operations are challenging to detect. In this paper, I
have proposed a new copy-move forgery detection scheme using adaptive over-segmentation and feature-
point matching. The Adaptive Over-Segmentation algorithm is proposed to segment the host image into non-
overlapping and irregular blocks adaptively according to the given host images; using this approach, for each
image, we can determine an appropriate block initial size to enhance the accuracy of the forgery detection
results and, at the same time, reduce the computational expenses. Then, in each block, the feature points are
extracted as block features, and the Block Feature Matching algorithm is proposed, with which the block
features are matched with one another to locate the labeled feature points; this procedure can approximately
indicate the suspected forgery regions. Subsequently, to detect the more accurate forgery regions, we propose
the Forgery Region Extraction algorithm, in which the labeled feature points are replaced with small
superpixels as feature blocks, and the neighboring feature blocks with local color features that are similar to
the feature blocks are merged to generate the merged regions. Next, the morphological operation is applied to
the merged regions to generate the detected forgery regions.. 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] Jessica Fridrich, David Soukal, and Jan Lukáš, “Detection of Copy-Move Forgery in Digital Images”, in Proc.
Digit. Forensic Res. Workshop, Cleveland, OH, Aug. 2003.
[2] A.C. Popescu and H. Farid, “Exposing digital forgeries by detecting duplicated image regions”, Dept. Comput. Sci.,
Dartmouth College, Hanover, NH, USA, Tech. Rep. TR2004-515, 2004
[3] Saiqa Khan, Arun Kulkarni, “Robust Method for Detection of Copy-Move Forgery in Digital Images”,
International Conference on Signal and Image Processing, 2010.
[4] L. Fitzpatrick and M. Dent, “Region Duplication Detection Using Image Feature Matching”, Ieee Transactions On
Information Forensics and Security, Vol. 5, No. 4, 2010.
[5] Jian Li, Xiaolong Li, Bin Yang and Xingming Sun, “Segmentation-Based Image Copy-Move Forgery Detection
Scheme”, IEEE Transactions on Information Forensics and Security, Volume: 10, Dec 2014.
[6] Chi-Man Pun, Xiao-Chen Yuan and Xiu-Li Bim, “Image Forgery Detection Using Adaptive Over segmentation and
Feature Point Matching”, IEEE Trans. Inf. Forensics Security, Vol. 10, No. 8, August 2015.
[7] Vincent Christlein, Christian Riess, Johannes Jordan, CorinnaRiess, and Elli Angelopoulou, “An Evaluation of
Popular Copy-Move Forgery Detection Approaches”, IEEE Transactions On Information Forensics And Security,
Vol. 7, No. 6, December 2012.
[8] M. Kirchner And R. Bohme, "Hiding Traces of Resampling in Digital Images", Information Forensics And
Security, IEEE Transactions, Vol. 3, Pp. 582-592, 2008
[9] H. Huang, W. Guo, and Y. Zhang, “Detection of copy-move forgery in digital images using SIFT algorithm”, in
Proc. Pacific-Asia Workshop Comput. Intell. Ind. Appl. (PACIIA), Dec. 2008, pp. 272–276.
[10] B.L. Shivakumar and S.S. Baboo, “Detection of region duplication forgery in digital images using SURF”, IJCSI
Int. J. Comput. Sci. Issues, vol. 8, issue 4, no. 1, pp. 199–205, 2011.
BIOGRAPHY OF AUTHOR
Mr. Anil Gupta is working as a Assistant Professor with the Department of Information
Technology, Model Institute of Engineering and Technology, Kot Balwal, Jammu, India. Mr.
Anil Gupta has over Sixteen years of working experience. Mr. Anil Gupta has substantial
academic work to his credit. As a distinguished academician he has published considerable
number of research papers in various reputed national and international journals and he has also
presented research papers in various national and international conferences. His has strong
orientation towards Programming and specific areas of interest are Programming in C, C++, Java
and MATLAB

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