Copy Move Forgery Detection Using Key-Points StructureA Thesis Submitted

in Partial Fulfillment of the Requirements

for the Degree of

Master of Sciencein

Cyber Security

by

Vinod Parihar14/MS/030

Under the Supervision ofDr. B. M. Mehtre

(Associate Professor)Center For Cyber Security

Institute For Development And Research In Banking Technology, Hyderabad

(Established by Reserve Bank of India)

COMPUTER SCIENCE AND ENGINEERING DEPARTMENTSARDAR PATEL UNIVERSITY OF POLICE, SECURITY AND CRIMINAL

JUSTICE

JODHPUR – 342304, INDIAMay, 2016

UNDERTAKING

I declare that the work presented in this thesis titled “Copy Move

Forgery Detection Using Key-Points Structure”, submitted to the

Computer Science and Engineering Department, Sardar Patel Uni-

versity of Police, Security and Criminal Justice, Jodhpur, for the

award of the Master of Science degree in Cyber Security, is my

original work. I have not plagiarized or submitted the same work for

the award of any other degree. In case this undertaking is found in-

correct, I accept that my degree may be unconditionally withdrawn.

May, 2016

Jodhpur

(Vinod Parihar)

ii

CERTIFICATE

Certified that the work contained in the thesis titled “Copy Move

Forgery Detection Using Key-Points Structure”, by Vinod Parihar,

Registration Number 14/MS/030 has been carried out under my su-

pervision and that this work has not been submitted elsewhere for a

degree.

May, 2016

( Dr. B. M. Mehtre)

(Associate Professor)

Center For Cyber Security,

Institute For Development And Research In

Banking Technology, Hyderabad

(Established by Reserve Bank of India)

iii

Acknowledgment

I would like to take this opportunity to express my deep sense of gratitude to all who

helped me directly or indirectly during this thesis work.

First, I would like to thank my supervisor, Associate Professor Dr. B. M. Mehtre , for

being a great mentor and the best adviser I could ever have. His advise, encouragement

and critics are source of innovative ideas, inspiration and causes behind the successful

completion of this dissertation. The confidence shown on me by him was the biggest

source of inspiration for me. It has been a privilege working with him from last five

months.

I wish to express my sincere gratitude to Dr. Bhupendra Singh , Vice Chancellorand Sh. M.L. Kumawat, (Former) Vice Chancellor, for providing me all the facilities

required for the completion of this thesis work.

I would like to express my sincere appreciation and gratitude towards faculty members

at S.P.U.P., Jodhpur, especially Mr. Arjun Choudhary, Mr. Vikas Sihag for their

encouragement, consistent support and invaluable suggestions. I thanks to Mr. VT Manu

PhD. scholar, who helped me, guided me at the time I needed the most. Whenever I get

nervous, I used to talk with my colleagues. They always tried to encourage me, without

all mentioned above, this work could not have achieved its goal.

iv

Finally, I am grateful to my father Mr. Kanti Lal, my mother Mrs. Jasoda Devi for

their support. It was impossible for me to complete this thesis work without their love,

blessing and encouragement.

Vinod Parihar

v

Biographical Sketch

Vinod Parihar

Inside 3rd Pole Mahamandir, Jodhpur,Rajasthan PIN-342010

E-Mail: vinodparihar121@gmail.com, Mob. No. +91-9660073121

Father’s Name : Mr. Kanti Lal

Mother’s Name : Mrs. Jasoda Devi

Education

• Pursuing Master of Science in Computer Science & Engineering branch from S.P.U.P.,

Jodhpur, 2016.

• B.Tech. in Computer Science and Engineering from place with 68% in 2014.

• Intermediate from Shri Sumer S. S. School, Jodhpur with 68% in 2000.

• High School from D.B.V.K.Sec.School, Jodhpur, with 66% in 1998.

vi

Dedicated to My Loving Family for their kind love & support.To my friends for showing confidence in me.

vii

}Insanity is doing the same thing, over and over again, but expecting different re-sults.~

-Albert Einstein

viii

Synopsis

Modification of information of an image is an easy task as increasing number of images

editing tools and techniques are freely available on the net. This leads to wide spread used

forged images for various purposes intently and unintently. It is difficult to determine the

authenticity of the image. Inserting wrong information, modifying original information

of image for creating new image is known as image forgery. In copy-move image forgery,

a region of an image is copied and pasted on the same image.

Due to various geometric based attacks (translation, rotation and scaling) and post-operation

based attacks, Copy-Move Forgery Detection (CMFD) is not an easy task. Block-based

CMDF methods work well with all types of translation based geometric attacks. But

these methods do not work well for rotation and scaling and they are also slow compared

to key-points based methods. Key-points based methods work well with translation, ro-

tation and scaling. These methods are faster than block-based methods. But key-points

based methods have some limitation (these methods do not work well with homogeneous

regions etc.). These methods do not work well with types of rotation degree (from 0 to

330) and scaling (from 0.5 to 2.0).

In this thesis, Triangles of keypoints based CMFD Method is implemented which works

well on the images with translation, rotation and scaling. The proposed method has

overcome some disadvantages of reference method. Experimental result of the proposed

method shows improved performance compared to reference methods.

ix

x

Contents

Acknowledgment iv

Biographical Sketch vi

Synopsis ix

1 Introduction 11.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Image Forensics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Various techniques for tempering Image: . . . . . . . . . . . . . . . . . . 2

1.4 Various Techniques in Image Forgery Detection: . . . . . . . . . . . . . . 4

1.5 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Literature Survey 62.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2 Block Diagram of Process of CMFD . . . . . . . . . . . . . . . . . . . . 6

2.3 Classification of CMFD methods . . . . . . . . . . . . . . . . . . . . . . 7

2.3.1 Block-based algorithms . . . . . . . . . . . . . . . . . . . . . . 8

2.3.2 Keypoints-based algorithms . . . . . . . . . . . . . . . . . . . . 11

2.4 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

xi

3 CMFD using Key-Points Structure : Proposed Method 133.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2 Proposed Method: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.2 Steps of CMFD using Key-Points Structure: . . . . . . . . . . . . 14

3.3 Reference Method: CMFD by matching triangles of key-points: . . . . . 15

3.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3.2 Mean Vertex Descriptors based Triangle Matching method: . . . 16

3.3.3 Dataset of CMFD by Matching Triangles of key-points: . . . . . 17

3.4 Result Evalution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.4.1 Metric: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.4.2 Implementation of Proposed method: . . . . . . . . . . . . . . . 18

3.4.3 Graph Analysis: . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.5 Summery: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4 Texture based CMFD Method : Proposed Method 274.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.2 Overview of localized angular phase method: . . . . . . . . . . . . . . . 27

4.3 Proposed Method: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.4 Dataset : . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.5 Result Evalution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.5.1 Metric: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.5.2 Implementation of Proposed method: . . . . . . . . . . . . . . . 32

4.5.3 Result testing Through graph analysis of Proposed Method based

on Texture : . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5 Conclusion and Future Work 36

References 39

xii

List of Figures

1 Example for Copy-Move Forgery . . . . . . . . . . . . . . . . . . . . . 3

2 Example for Image Splicing Forgery [6] (a) first original image (b) Sec-

ond Original image (c) Forge Image by combining both (a) and (b) image. 3

3 Example for Image Retouching Forgery( Leftside original image and right-

side forge image.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

4 Image Forgery classification. . . . . . . . . . . . . . . . . . . . . . . . . 5

5 Block diagram of Detection of Copy-Move forgery . . . . . . . . . . . . 7

6 Classification of CMFD methods . . . . . . . . . . . . . . . . . . . . . 8

7 Desktop Application of Proposed method with circle points . . . . . . . 19

8 output image of Proposed method . . . . . . . . . . . . . . . . . . . . . 20

9 another output image of Proposed method . . . . . . . . . . . . . . . . . 20

10 Main form of Desktop application of Reference method . . . . . . . . . . 21

11 Sift Angle Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

12 Sift Vertex Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

13 Surf Angle Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

14 Surf Vertex Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

15 Comparative Result of Both method on Dataset 0 . . . . . . . . . . . . . 24

16 Comparative Result of Both method on Dataset 1 . . . . . . . . . . . . . 25

xiv

17 Comparative Result of Both method on Dataset 2 . . . . . . . . . . . . . 25

18 LAP feature in a block. . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

19 Super LAP feature in a block . . . . . . . . . . . . . . . . . . . . . . . . 30

20 Input Image into Desktop Application . . . . . . . . . . . . . . . . . . . 33

21 LAP points on Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

22 Output Image into Desktop Application . . . . . . . . . . . . . . . . . . 34

23 Output of another Image into Desktop Application . . . . . . . . . . . . 35

24 Result of Proposed method on Dataset [3], [4] and [20] . . . . . . . . . . 35

xv

List of Tables

1 Frequency based CMFD methods . . . . . . . . . . . . . . . . . . . . . 9

2 Intensity based CMFD methods . . . . . . . . . . . . . . . . . . . . . . 9

3 Moments based CMFD methods . . . . . . . . . . . . . . . . . . . . . . 10

4 Dimensionality based CMFD methods . . . . . . . . . . . . . . . . . . . 10

5 Keypoints-based CMFD methods . . . . . . . . . . . . . . . . . . . . . . 11

6 Metric for Testing Result of Proposed methods . . . . . . . . . . . . . . 18

7 Technology used for implementation of Proposed methods . . . . . . . . 19

8 Metric for Testing Result of Proposed methods . . . . . . . . . . . . . . 32

9 Technology used for implementation of Proposed methods . . . . . . . . 33

xvi

Chapter 1

Introduction

1.1 Overview

A picture is worth a thousand words. It is true in the case of the cyber-crime investigation.

An image is an important part of the digital evidence in cyber-crime. The image may be

contained various types of information likes crime scenes, location and position various

types of objects like body, weapons, size and shape of injury marks. The image has the

capability to show complete visual of crime scenes and locations of the evidence within

the crime scene. Any word document fails to do that.

Image is an important type of Digital information in digital world. Tempering Images

is easy task with the help various image editing tools and software. Tempered images

contain false information if tempered image uses for fun or entertainment then it is ok.

But if it uses for some illegal activities or misuse then it becomes necessary to detect

forgery from tempered image.

Image forensic is way of detecting image forgery. It finds out authentication of any image.

1

1.2 Image Forensics

Image Forensic is divided into three main branches as follows [22]

1. Image Source or Device Identification: the aim of this branch is to identify which

device or source was used to capture image.

2. Discrimination of computer generated Images: the aim of this branch is to

identify that the image is natural or synthetic.

3. Image Forgery Detection: this is used to identify that the image is authentic

or unauthentic image. If an image contains any forgery then this branch identify

forgery in the image.

1.3 Various techniques for tempering Image:

Various techniques are used for tempering image. Mainly Three types of tempering meth-

ods are used commonly as follows [22]:

1. Copy-move Forgery: This is very famous types of image tempering type. It is easy

to perform and difficult to detect on image. In this type image forgery, some part

of image is copied from a specific place on image and put it one or more than one

place within the same image.

2

Figure 1: Example for Copy-Move Forgery .

2. Splice Image Forgery: In this type tempering method, a region of image is pasted

another image or combined two or more image to generate a new image which has

various parts of all images.

Figure 2: Example for Image Splicing Forgery [6] (a) first original image (b) Second

Original image (c) Forge Image by combining both (a) and (b) image.

3. Image Retouching: in this method, there is increase or decrease certain feature of

the image using image editing tools. Image retouching is a process of image editing

3

for restoration or enhancement feature (like color, shape, contrast, brightness etc.)

of the image. This method is mainly used for advertisement, fashion, beauty etc.

Figure 3: Example for Image Retouching Forgery( Leftside original image and rightside

forge image.)

1.4 Various Techniques in Image Forgery Detection:

Mainly, Active and Passive are two different techniques of image forgery detection [22].

1. Active Technique: In this technique, original image is protected from tempering

through generating signature and embedding watermarking. Some preprocessing

methods (like watermarking and digital signature) apply on image in this technique.

There must applied preprocessing methods on image in advance. Otherwise active

technique fails to detect forgery from image.

2. Passive Technique: it is the opposite of Active technique. It does not use any

preprocessing methods for detecting forgery. There is not required any information

of original image at time of finding forgery in tempering image. Passive technique is

further divided into two categories like visual method and statistical method. Visual

method is worked on visual information like light deformation, brightness etc. Any

other information is not required in this method. Statistical method is more accurate

and convince. It is worked on image pixel information.

4

Figure 4: Image Forgery classification.

1.5 Organization of Thesis

The work did has been condensed in five chapters.

Chapter 1 describes the image forensic, various techniques of image forensics and differ-

ent types of image tampering methods.

Chapter 2 explains Copy-move forgery Detection (CMFD)in details.

Chapter 3 explains proposed work method (keypoints circle based CMFD method) in de-

tails with result and example.

Chapter 4 explains proposed work method (texture based CMFD method) in details with

result and example.

Chapter 5 The conclusions and the scope of further work.

5

Chapter 2Literature Survey

2.1 Introduction

Copy move forgery is easiest form in various types of image forgery. It is very simple

to use to temper an image. In copy-move forgery, a region of the image is copied and

pasted on the same image. A group of pixel of image is copied and move another part of

image pasted it on same image. This type is copy-move image forgery. First a specific

region of the image is copied and it is pasted on the same image, is known as Copy-move

forgery. Its used for hiding unwanted region of the image or increasing the numbers of

specific region on the image. Both regions in copy-move forgery have similar properties

like noise level, color and texture. Therefore it is difficult to detect this type of image

forgery.

2.2 Block Diagram of Process of CMFD

A common process is used by various methods for detecting Copy-move forgery.

6

Block diagram of process of CMFD is shown in figure 1. Block diagram shows com-

mon steps in form of pipeline.

Figure 5: Block diagram of Detection of Copy-Move forgery

1. Preprocessing: in this step, some methods covert input image data into gray-scale

type image.

2. Block based feature detection: various CMFD methods is used block based fea-

ture extraction methodology.

3. Key-point based feature detection: various CMFD methods is used key points

based feature extraction methodology.

4. Matching: in this process, comparing various feature to others and find similar

feature on image

5. Filtering: this is used for remove false negative match in matching process.

6. Post processing: there is grouping of matching regions on the image.

2.3 Classification of CMFD methods

CMFD methods are divided into two categories [1]:

1. Block-based algorithms

2. Keypoints based algorithm

7

Figure 6: Classification of CMFD methods

2.3.1 Block-based algorithms

In block based methods, an image is divided into overlapping blocks and extract feature

from blocks. Various different methods are used for extract the feature from the block

like Frequency based methods (like DCT (Discrete cosine transform), DWT (Discrete

wavelets transform) and FMT (Fourier-Mellin Transform)), Moments based methods (like

Zernike, Blur and Hu), Dimensionality reduction methods (like PCA (Principal compo-

nent analysis), SVD (singular values Decomposition)), etc. Block based CMFD methods

work accurate and robust in case of the homogeneous image, simple and complex scene

image. But most of these methods fail to detect forgery part on the image when forgery

part is rotated and scaled.

On the base of extracting feature from blocks, Block Based algorithm is further di-

vided into 4 groups as follows[1]

(a) Frequency based: various frequency transform methods are available for trans-

form. Various CMFD methods of this group is listed as follows

8

Table 1: Frequency based CMFD methodsName of Methods Feature lenthsDCT 256

DWT 256

FMT 256

Discrete cosine Transformation based CMFD: First DCT based CMFD method

is proposed by J. Fridrich et al. [5]. In this method, DCT applied on all small blocks

of image and quantized DCT coefficient. After this Similar DCT coefficient block

mark as tempered part on image. Another DCT based Method is suggested by N.

D. Wandji et al. [7]. Feature vector extracted from DCT coefficient of each block of

image and sorted feature using lexicography. Similar pairs of blocks were marked

as tempered part of the image. This method works efficient in case of rotation,

scale, blur and noise.

DWT based CMFD: Khan et al. [8] proposed a DWT based CMFD methods which

methods applied DWT for compress image up to the fixed level. This fixed level

depends on the size of image. This process reduces the dimensional of image.

FMT based CMFD: S. Bayram el al. [13] proposed a CMFD method based on

FMT (Fourier-Mellin Transform). Counting bloom filter method is used to improve

detecting process of this method. This method is invariant with rotation (up to 10)

and scaling (up to 10

(b) Intensity based: various frequency transform methods are available for transform

Table 2: Intensity based CMFD methodsName of Methods Feature lenthsLuo 256

Circle 256

Circle Block Based CMFD: J. Wang el al. [18] proposed a CMFD method based

on Circle Block. In this method, Gaussian pyramid is used for reduce dimension.

After this, Circle feature is extracted from four circle block. Lexicographical sorting

9

is used for detecting similar circle feature. This proposed method is invariant with

rotation and post-processing operation like noise, blurring and jpeg compression.

W. Luo et al. [19] proposed a CMDF methods based on intensity feature. In this

method, each block is represented by seven characteristic features. First three fea-

tures are determined by average value of RGB component and next four features

are determined by Y channel value block. Lexicographical sorting is further used

for searching similar feature of blocks on the image.

(c) Moments based: various frequency transform methods are available for transform

Table 3: Moments based CMFD methodsName of Methods Feature lenthsZernike 256

Blur 256

HU 45

Zernike moments based CMFD: S. Ryu el al. [11] proposed a CMFD methods

based on Zernike moments. Proposed method work invariant with rotation trans-

formation and post-operation like noise, jpge compression and blurring operation.

This method fails with scaling transformation.

(d) Dimensionality based: various frequency transform methods are available for

transform

Table 4: Dimensionality based CMFD methodsName of Methods Feature lenthsPCA -

SVD -

KPCA 192

PCA based CMFD: Asda A. Popescu et al. [10] proposed CMFD methods based

on PCA (Principal component analysis). In this method, PCA method is applied

10

on small sub-block of the image and lexicographically sorting is used for detect-

ing facsimile regions on the image. This method is invariant with noise and jpeg

compression.

2.3.2 Keypoints-based algorithms

In the case of key-points based CMFD methods, key-points are detected on the image.

Key-points is assigned to points on the image having a specific feature (like scale invariant

feature in SIFT algorithm [14]). They are spatial locations or points in the image that

define what is the interesting feature in the image. These methods are faster as compare

to block based CMFD method and these methods performance is good in case of all types

of transformations of facsimile regions on the image. There are various key-point based

methods used for image forgery detection like SIFT (Scale Invariant Feature Transform

[14]), SURF (Speeded-Up Robust Features [21]) and etc.

Table 5: Keypoints-based CMFD methodsName of Methods Feature lenthsSIFT 128

SURF 64

ORB -

SIFT based CMFD: various CMFD methods is used SIFT methods as based method.

H. Huang et al. [9] proposed a CMFD methods based on SIFT key-point descriptors.

In this method, key-points divided into two sets and one set contains one element and

another set contains remain of key-points. After getting two sets, there is apply BBF

(Best-Bin-First) method and save matching key-points. This process is repeated for all

key-points. This method is also worked well in case of rotation and scaling transform.

SURF based CMFD: V. T. Manu at al. [16] proposed a CMFD method based on segmen-

tation and SURF [21]. In this method, simple linear iterative clustering (SLIC) method

is used for image segmentation and SURF method is used for extract key-points on the

image. After this, each region denoted by label on based of key-points in that region.

Similar region on the image find out by label and matched key-points within the region.

11

ORB based CMFD method: Y. zhu et al. [17] proposed a CMDF method based on ORB

key descriptor. In this method, ORB key descriptor is used to extract key-points on the

image. Hamming distance is used for matching ORB features between two key-points.

RABSAC method is used to remove false result (wrong matched key-pomts).

2.4 Problem Statement

CMFD is an difficult task to detect with all types of attacks. Block based CMFD methods

are better in case of Translation. But these types CMFD method is not well with Ge-

ometric based attacks like rotation, scaling. Key-points based CMFD methods are well

with Geometric based attacks like rotation, scaling. these types method have also some

limitation like it is failed in case of homogeneous area on the image.

12

Chapter 3

CMFD using Key-Points Structure :

Proposed Method

3.1 Introduction

In copy-move type of image forgery, a facsimile of the specific region of the image put

on the same image. Both regions are having similar properties like texture, color, noise,

illumination etc. Therefore, noise and illumination based image forgery detection meth-

ods fail to detect copy-move Forgery (CMF). This types image forgery conceals some

important area of the image under facsimile region. Sometimes, this is used to increase

the number of specific regions of the image and represents false information in the im-

age. Before tampering Image through CMF, various transformations like rotation, scaling,

translation, distortion and combination of more than one transformation can be used on

the facsimile region for making difficult the process of CFM detection (CMFD). Post-

processing methods are used for improving qualities of the image. In post-processing,

the information of original and facsimile region is change slightly. Each Post-processing

13

operation like compression, blurring, color change, brightness change and contrast ad-

justment produce a different impact on CMFD process.

3.2 Proposed Method:

3.2.1 Introduction

keypoints are extracted on image using local feature of Image like Scale invariant place. In

CMDFD, keypoints orientation on original and forgery part are equal and same structure.

Feature value (descriptor values) of these keypoint are similar on both part. Large number

of keypoints on the image is main problem for finding similar orientation of keypoints

regions on the image. In this a work, we find nearest keypoint of a particular keypoint

on the image and considered this keypoint as center of circle. the distacne between both

keypoint should be minimum and greater than threshold distance value. we considered

this distance as radius of circle. the number of circle on the image is less than number of

keypoints. therefore, the number of comparison is reduced. each circle is represented by

mean of keypoint on radius.

3.2.2 Steps of CMFD using Key-Points Structure:

1. SIFT method is used to extract key-points on image.

2. Creating Circle:

• Distance of a key-point from near key-points is determined.

• This distance must be greater than 2 and less than 100. It should minimum

from all near key-points. i.e. 2 < d <100 and d should be minimum distance

or nearest points.

• This distance is considered as radio of that key-point and draw circle on image.

• Make group of both these two points.

3. Mean of feature vector of each group is determined.

| Vmij − Vmik |≤ THv (1)

14

Here, Ckj is mean feature of a group of two key-points and THC is threshold value.

4. Matching circle process: Similar feature circle is marked as similar region on the

image.

3.3 Reference Method: CMFD by matching triangles of

key-points:

3.3.1 Introduction

E. Ardizzone et al. [3] proposed a novel approach which is based on key-points struc-

ture analysis through triangle of Key-point. In Copy-move forgery, original regions and

facsimile regions have similar properties like texture, color etc. structure of key-points

on original and facsimile part is similar to each other. In this method, common key de-

tector methods like SIFT SURF and Harris are used for detecting key-points on image.

Delaunay triangulation method is used for creating triangle using key-points on image.

Delaunay triangulation methods creates non-overlapping triangle onto key-points. These

methods find similar triangles on based of similar color, angle and mean vertex descrip-

tor. In this research paper, there are proposed two different methods like Angle method

and Vertex methods. In Angle method, Angle and color of triangle is used for matching

triangles. In homogeneous case, various triangles have similar color and angle. There-

fore in this case, the number of false matching of triangle increases. In vertex methods,

mean value of vertex descriptor is used for matching triangle. These CMFD methods are

working well on various transform like translation, rotation and scaling. In case of similar

scenes and small number of triangle, these methods produce well output. But in case of

complex scenes and large number of triangle, these methods do not produce well output.

Mainly in this research paper, there are two different methods for CMFD as follows:

1. Color and Angle based triangle matching method

2. Mean vertex descriptors based triangle matching method

These two methods are used for matching triangles and find out copy-move forgery area

on image.

15

These two methods are used for matching triangles and find out copy-move forgery

area on image.

Primary Steps: creating triangle on image is primary steps, which is used by both

methods.

1. Key-points are extract on the image using Key-point detector methods like SIFT,

SURF etc.

2. Arbitrary points are added on the borders of the image.

3. Triangle mesh is constructed onto key points of the image.

3.3.2 Mean Vertex Descriptors based Triangle Matching method:

There are used various steps in this method as follows:

1. Mean Vertex Descriptor (MVD) for each triangle is computed using descriptor

value of each vertex of the triangle. The Mean Vertex Descriptor Vmi for each

triangle calculates using equation (2).

Vmi = (V1i+ V2i+ V3i)/3 (2)

Where Vj = 1.3 i=1, 2, 3 are the descriptor value of three vertex of triangle. Vmi is

the mean vertex descriptor of triangle on the images.

The mean vector for each triangle is n-value array, where n equals to 128 in case of

SIFT and 64 in case of SURF.

2. For sorting Triangle, L1 norm of MVDs of all triangle is used.all triangles are sorted

using MVDS value.

3. In triangle comparison presses, MVD value of triangle is compared to the further

triangles in sorted list of triangle, within a computed fixed window of size ws (fixed

number of triangles).

16

4. If i and j are the number indexes value of two triangle in sorted list of triangle and

Vmi , Vmj are the MVDs of triangles:

| Vmi− Vmj |≤ THv (3)

(j − i) < fws (4)

Where, threshold THv is equal to 0.25 and fws is the size of fixed window.

5. To further remove false positives, they compute the centroids points of triangles

and apply Random Sample Consensus (RANSAC) method to the set of matching

centroids points, to find the set of inliers point. If total number of matches is below

4, RANSAC cannot apply for removing false positive.

3.3.3 Dataset of CMFD by Matching Triangles of key-points:

For result testing purpose, we choose dataset [3] of variety of images.

1. dataset [3]: there are four different directories which contains different types of

geometric based attack on images. Directory D0 contains 50 tampered images and

tampered image contains only translation attack. Directory D1 contains images

which contains only rotations attack. D1 is further divided into three subdirectory

as follow

• D1.1 (rotation range -25 to 25).

• D1.2 (rotation range 0 to 360).

• D1.3 (rotation range -5 to 5).

D2 is further divided into two subdirectory as follow

• D2.1 (scaling range 0.25 to 2).

• D2.2 (scaling range 0.75 to 1.25).

17

3.4 Result Evalution

3.4.1 Metric:

For find out Accuracy of Proposed methods, we are used following parameter as follow:

Table 6: Metric for Testing Result of Proposed methodsSerialNum-ber

ParameterName

Formulas Description of Parameter

1. True Pos-itive Rate(TPR)

(TP)/(TP+FN) here TP stands for True Positive or Forgepart identify as forge part on Tamperedimage FN stands for False Negative orForge part does not identify as forge parton Tampered image

2. True Neg-ative Rate(TNR)

(TN)/(TN+FP) here TN stands for True Negative or Orig-inal part identify as forge part on Tam-pered image FP stands for False Positiveor Original part identify as forge part onTampered image

3. Accuracy (TP+TN)/(TN+FP+TP+FN) Accuracy define method accuracy in nu-meric form.

4. False Neg-ative Rate(FNR)

FN/(FP+TN) this is represent rate of false matching orworng output.

5. False Pos-itive Rate(FPR)

FP/(FP+TP) this is represent rate of true matching orcorrect output

3.4.2 Implementation of Proposed method:

Technology used: For Implementation of this proposed method, we used Python Lan-

guage with Opencv. there are list of component of implementation as follow:

18

Table 7: Technology used for implementation of Proposed methodsSerialNumber

component name Description of Component

1. Operating System we used Ubuntu OS in which

python programming is easier

than Window OS.

2. Programming Language Python 2.7

3. Modul of Python Open cv2, scipy, mat-

plotlib.collections, sklearn,

numpy, triangle and sys

Snap Shot of Tools with Output:

this tool is a Desktop application which represent all image on single frame with

parameter.

1. Main Window frame of Proposed method:

Figure 7: Desktop Application of Proposed method with circle points

19

Figure 8: output image of Proposed method

Figure 9: another output image of Proposed method

20

Snap shot of Reference Application :

1. Main Window frame:

Figure 10: Main form of Desktop application of Reference method .

2. Sift Angle Method Frame:

21

Figure 11: Sift Angle Method

3. Sift Vertex Method Frame:

Figure 12: Sift Vertex Method

22

4. Surf Angle Method Frame:

Figure 13: Surf Angle Method

5. Surf Vertex Method Frame:

23

Figure 14: Surf Vertex Method

3.4.3 Graph Analysis:

Figure 15: Comparative Result of Both method on Dataset 0

24

Figure 16: Comparative Result of Both method on Dataset 1

Figure 17: Comparative Result of Both method on Dataset 2

25

3.5 Summery:

These both methods works well with translation, rotation and scaling. Proposed have

some advantage over reference method as follow:

1. In reference, Delaunay triangulation produces a complex structure of triangles with

large number of keypoints. therefore, These methods does not work well with com-

plex scene image(this type image have large number of keypoints and triangle.).In

case of Proposed method, number of circle is less than number key-points. there-

fore Proposed method word well in case of complex image comparative reference

method.

2. Proposed method is faster compare to reference method in case of increasing num-

ber of Key-points on images.

3. In reference, Mean of three vertex of triangle is used for comparing triangle. In case

of Proposed method, Mean of two point of radius of circle is used for comparing

circle.

26

Chapter 4Texture based CMFD Method :

Proposed Method

4.1 Introduction

Localized angular phase (LAP) method is proposed by K. M. Saipullah et al. [2]. This is

an texture descriptor method which is robust in case of illumination, blurring and scaling.

LAP method takes local image pixel and convert it into polar space using polar space

using fixed-radius polar space function p(r,/theta ). After this, fourier transform method

is used to convert polar space value into frequency signal. LAP method is used phase

information for texture descriptor. Phase information of each block is represent by 8-bit

number after analyzing phase information.

4.2 Overview of localized angular phase method:

Mainly in this research paper, there are two different methods for CMFD as follows:

1. Image I is divided into 3x3 sub-image (overlapping blocks).

27

2. This 3 3 sub-image is then change into fixed-radius polar space p(r,) using (5):

p(r, θ) = s(x, y), r = 1, θ = 0◦, 40◦, ..., 320◦, x = rcosθ, y = rsinθ (5)

Here, s(0,0) is center point of 3x3subimage.Some s(x, y) points do not fall on the

rectangular grid. These values need to be interpolated using bilinear interpolation

given as follow:

s(x′, y′) = a1x′ + a2y′ + a3x′y′ + a4 (6)

Here, a1, a2, a3 and a4 are four neighbors of point s(x, y). Because r = 1, p(r, θ )

can be seen as a 1D discrete signal with nine samples.discrete signal is represented

by p(m), n = 0, 1, . . . , 8.

3. The Fourier transform of p(m) are given by

p(k) =N−1∑n=1

p(n)e(−2i/Nkn) (7)

Where N is the number of samples in p(m), and for 3 3 sub-image, N is 9. the

discrete signals p(m) are converted to the Fourier coefficients P(k).

4. After the Fourier transform, the values of nine complex coefficients P(0), P(1),. . .

, P(8) are obtained. The P(0) is the DC value of the Fourier transform and contains

no phase information; thus it is excluded from the selected coefficients.

5. four non redundant complex coefficients are selected, whereby half of the complex

coefficients are either P(1), P(2), P(3), P(4) or P(5), P(6), P(7), P(8).

6. C matrix contain the information of these four complex coefficients given by

C = [ReX(4)ImX(4)ReX(3)ImX(3)ReX(2)ImX(2)ReX(1)ImX(1)] (8)

7. Matrix C is quantized into 8-bit binary code by using the following formula:

b(k) =

1, ifCk ≥ 0

0, otherwise(9)

Where b(k) is the sign of each coefficient.

28

8. By arranging b(1), b(2), . . . , b(7), the 8 bit binary code can be formulated, and a

binomial factor is assigned as 2 for each b(k); hence, it is possible to transform (5)

into a unique LAP number, given by

LAP =8∑

k−1

b(k)2k−1 (10)

This LAP is a decimal value between 0 and 255 resulting from the 8-bit binary

code.

These two methods are used for matching triangles and find out copy-move forgery area

on image.

4.3 Proposed Method:

In our proposed work, we used LAP methods for Extracting feature from each block of

image.

1. Feature extraction: First, we divide the image into overlapping block of 3x3 sizes.

LAP method is applied on each block and extract LAP feature from block. Center

pixel of 3x3 blocks has this value. Instead of boarder pixel, all pixel of image have

LAP feature value.

2. Calculate super LAP feature of block In this step, we calculate super LAP

feature of each block as follow

M =Max(L1, L2, ..., L9) (11)

SLAPi =M − Li (12)

29

Figure 18: LAP feature in a block.

Figure 19: Super LAP feature in a block

3. Grouping of similar feature value After Super LAP feature extraction, we make

group of pixel having similar Super LAP feature value. For example, we apply

Super LAP methods on 1(a). After getting Super LAP feature, we create group of

similar feature value as follows: Groups of similar feature: 47: 170776, 46: 61154,

40: 14978, 63: 5765, 44: 5029, 42: 1722, 32: 1615, 43: 77, 60: 5 There are total

30

nine group of similar feature. Total 170776 pixels have Super LAP value 47. Total

61154 pixels have Super LAP value 46 and so on. In 1(b), white pixels LAP value

is 47.

4. Selecting group: We select group of least number of pixels value which Super

LAP value represent a specific place of texture.

5. Matching Feature into selecting group In matching process, we compare Super

LAP feature of selected block with each other. If j and k are number of block on

the image then following condition is used for checking.

8∑i=0

| SLAPji − SLAP

ki |= 0 (13)

If Sum of Absolute deviation of Super LAP is equal to zero, then both block j and

k block are considered similar.

4.4 Dataset :

For result testing purpose, we choose three different dataset [3], [4] and [20] of variety of

images.

1. dataset [3]: there are four different directories which contains different types of

geometric based attack on images. We take only Directory D0 images Directory

which contains 50 tampered images and tampered image contains only translation

attack.

2. Dataset [4]: this dataset contains more than 10000 images which contains geomet-

ric and post-operation based attack. We choose only 40 images for testing proposed

methods on translation.

3. Dataset [20]: this dataset contains different types of atmospheric images. We

choose only 100 translation image for testing result.

31

4.5 Result Evalution

4.5.1 Metric:

For find out Accuracy of Proposed methods, we are used following parameter as follow

[23]:

Table 8: Metric for Testing Result of Proposed methodsSerialNum-ber

ParameterName

Formulas Description of Parameter

1. True Pos-itive Rate(TPR)

(TP)/(TP+FN) here TP stands for True Positive or Forgepart identify as forge part on Tamperedimage FN stands for False Negative orForge part does not identify as forge parton Tampered image

2. True Neg-ative Rate(TNR)

(TN)/(TN+FP) here TN stands for True Negative or Orig-inal part identify as forge part on Tam-pered image FP stands for False Positiveor Original part identify as forge part onTampered image

3. Accuracy (TP+TN)/(TN+FP+TP+FN) Accuracy define method accuracy in nu-meric form.

4. False Neg-ative Rate(FNR)

FN/(FP+TN) this is represent rate of false matching orworng output.

5. False Pos-itive Rate(FPR)

FP/(FP+TP) this is represent rate of true matching orcorrect output

4.5.2 Implementation of Proposed method:

Technology used: For Implementation of this proposed method, we used Python Lan-

guage with Opencv. there are list of component of implementation as follow:

32

Table 9: Technology used for implementation of Proposed methodsSerialNumber

componentname

Description of Component

1. Operating Sys-

tem

we used Ubuntu OS in which

python programming is easier

than Window OS.

2. Programming

Language—

Python 2.7

3. Modul of Python Open cv2, scipy, collections

and cmath

Snap Shot of Tools with Output:

this tool is a Desktop application which represent all image on single frame.

Localized Phase Method Frame:

Figure 20: Input Image into Desktop Application

33

Figure 21: LAP points on Image

Figure 22: Output Image into Desktop Application

34

Figure 23: Output of another Image into Desktop Application

4.5.3 Result testing Through graph analysis of Proposed Method basedon Texture :

Figure 24: Result of Proposed method on Dataset [3], [4] and [20]

35

Chapter 5Conclusion and Future Work

Copy-Move Forgery Detection (CMFD) methods work for detecting all types of copy-

move forgery attack like geometric based attack (rotation, translation, and scaling) and

post-operation based attack (blurring, noise, compression etc.).

summary of our study is as follows:

• Block-based methods work well with translation and result accuracy is also good.

but these method fail with rotation and scaling types attacks.

• Keypoints based methods are better in geometric based attacks.

• Keypoints structure based CMFD method are also work well in case of all types of

rotation and scaling.

• keypoints based CMFD methods does not work well with homogeneous area on the

image.

In our First proposed method, we try to improve [3] CMFD methods. We used the

circle based concept to reduce the number of key-points. This method works well with

36

rotation, translation and scaling. In our another proposed methods, we try to detect CMFD

on Texture based Feature like LAP feature. This works well with translation and some

types of rotation and scaling images. This method works well with blurring, illumination

change types images.

Future work: Key-points based CMFD methods work well with geometric and post-

operation based attack. But these methods fails in case of homogeneous area on the image

which Key-point based CMFD method does not extract keypoints. In case of complex

image, these methods generate large number of key-points and take processing time equal

to block-based method. In our future work, we will implement a method which will work

well on both homogeneous and non-homogeneous types image, all types of geometric

and post-operation based attack. for this method, we need a keypoint extraction method

which extract keypoint on homogeneous and non-homogeneous area on the image.there

is also need various types of geometric method for analysis of keypoints orientation on

the image.

37

Author’s Publication

• Vinod Parihar V.T. Manu and B.M. Mehtre, “Copy-Move Forgery Detection using

Key-Points Orientation”, Submitted to IEEE Trans. on Information forensics and

Security, in May, 2016.

38

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