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iii

To the Source of Kindness Who Provided Me with Ambition,

My Parents “Assoc. Prof Dr. Ismael and Mrs. Asma”.

To My Beloved Wife “Amina”, for her Love and Support this Research

Would Have Never Been Completed.

To My Brother “Ibrahim” and My Sisters “Noor” and “Marwa” for

being patience, supportive, and understanding.

To all my family, my relatives and friends receives my deepest gratitude

and love for their patience and support during the years of my study.

iv

ACKNOWLEDGEMENT

Thanks to Allah SWT for everything I was able to achieve and for everything

I tried but I was not able to achieve.

First of all, I would like to take this opportunity to gratefully acknowledge the

wholehearted supervision of Professor Dr. Ghazali bin Sulong during this work. His

dedication, skillful guidance, helpful suggestions and constant encouragement made it

possible for me to deliver a dissertation of appreciable quality and standard.

I am forever indebted to my parents for their patience and understanding,

alleviating my family responsibilities and encouraging me to concentrate on my study.

Finally, and most importantly, I would like to express special thanks to my

brothers, sisters and friends for their support when it was most required. Without their

help and encouragement, this study would not have been completed.

In addition, I want to thank my beloved friends Dr. Mohammed Al-Ali, Dr.

Ahmed Abdullah Ahmed, Dr. Akeel Shebeeb, Dr. Mohamed Shaban, Dr. Saeed Al

Hameed, and Omar Alzaide for their constant support through good and bad times.

Thank you for being the friend that I always wanted, I always needed and I always

deserved Finally, I would like to offer my special thanks to the faculty of computing

staff, your help will never be forgotten.

v

ABSTRACT

Sophisticated digital video editing tools has made it easier to tamper real videos

and create perceptually indistinguishable fake ones. Even worse, some post-processing

effects, which include object insertion and deletion in order to mimic or hide a specific

event in the video frames, are also prevalent. Many attempts have been made to detect

such as video copy-move forgery to date; however, the accuracy rates are still

inadequate and rooms for improvement are wide-open and its effectiveness is confined

to the detection of frame tampering and not localization of the tampered regions. Thus,

a new detection scheme was developed to detect forgery and improve accuracy. The

scheme involves seven main steps. First, it converts the red, green and blue (RGB)

video into greyscale frames and treats them as images. Second, it partitions each frame

into non-overlapping blocks of sized 8x8 pixels each. Third, for each two successive

frames (S2F), it tracks every block’s duplicate using the proposed two-tier detection

technique involving Diamond search and Slantlet transform to locate the duplicated

blocks. Fourth, for each pair of the duplicated blocks of the S2F, it calculates a

displacement using optical flow concept. Fifth, based on the displacement values and

empirically calculated threshold, the scheme detects existence of any deleted objects

found in the frames. Once completed, it then extracts the moving object using the same

threshold-based approach. Sixth, a frame-by-frame displacement tracking is

performed to trace the object movement and find a displacement path of the moving

object. The process is repeated for another group of frames to find the next

displacement path of the second moving object until all the frames are exhausted.

Finally, the displacement paths are compared between each other using Dynamic Time

Warping (DTW) matching algorithm to detect the cloning object. If any pair of the

displacement paths are perfectly matched then a clone is found. To validate the

process, a series of experiments based on datasets from Surrey University Library for

Forensic Analysis (SULFA) and Video Tampering Dataset (VTD) were performed to

gauge the performance of the proposed scheme. The experimental results of the

detection scheme were very encouraging with an accuracy rate of 96.86%, which

markedly outperformed the state-of-the-art methods by as much as 3.14%.

vi

ABSTRAK

Kecanggihan alat penyuntingan video digital telah membuat lebih mudah

untuk mengubah video gangguan sebenar dan memalsukannya supaya ia tidak dapat

dibezakan. Lebih buruk lagi, terdapat beberapa kesan pasca-pemprosesan, yang

berleluasa termasuk sisipan dan penghapusan objek untuk meniru atau

menyembunyikan peristiwa tertentu dalam bingkai video. Pelbagai cubaan telah dibuat

untuk mengesan pemalsuan video salinan-langkah sehingga kini; walau

bagaimanapun, kadar ketepatan masih tidak mencukupi dan ruang untuk

penambahbaikan adalah terbuka luas dan keberkesanannya terbatas kepada

pengesanan bingkai dan kawasan setempat yang tidak diganggu. Oleh itu, satu skim

pengesanan baru telah dibangunkan untuk mengesan pemalsuan dan meningkatkan

ketepatan. Skim ini melibatkan tujuh langkah utama. Pertama, ia menukarkan video

merah, hijau dan biru (RGB) ke dalam bingkai skala kelabu dan menganggap mereka

sebagai imej. Kedua, ia menyekat setiap bingkai ke dalam blok bukan pertindihan

piksel setiap satu bersaiz 8x8. Ketiga, bagi setiap dua bingkai berturut-turut (S2F), ia

menjejaki dua salinan setiap blok dengan menggunakan teknik pengesanan dua

peringkat yang dicadangkan dan melibatkan carian Berlian dan Pengubah Slantlet

untuk mencari blok pendua. Keempat, bagi setiap pasangan blok pendua daripada S2F,

ia mengira anjakan menggunakan konsep aliran optik. Kelima, berdasarkan nilai-nilai

anjakan dan ambang pengiraan empirikal, skim ini mengesan kewujudan sebarang

objek terpadam yang dijumpai di dalam bingkai. Setelah selesai, ia kemudiannya

mengekstrak objek yang bergerak menggunakan pendekatan berasaskan ambang-

sama. Keenam, pengesanan anjakan bingkai demi bingkai dilakukan untuk mengesan

pergerakan objek dan mencari laluan anjakan objek yang bergerak. Proses ini diulangi

untuk satu lagi kumpulan bingkai bagi mencari jalan anjakan seterusnya untuk objek

kedua yang bergerak sehingga kesemua bingkai habis. Akhirnya, laluan anjakan

dibandingkan antara satu sama lain dengan menggunakan algoritma sepadan

Lengkungan Masa Dinamik (DTW) yang hampir sama untuk mengesan objek

pengklonan. Jika mana-mana pasangan daripada laluan anjakan merupakan bandingan

yang sempurna maka pengklonan akan ditemui. Bagi mengesahkan proses ini, satu siri

eksperimen berdasarkan set data dari Perpustakaan Universiti Untuk Analisis Forensik

(SULFA) dan Video Mengganggu Set Data (VTD) telah dijalankan untuk mengukur

prestasi skim yang dicadangkan. Keputusan eksperimen skim pengesanan sangat

menggalakkan dengan kadar ketepatan 96.86%, yang ketara mengatasi kaedah terkini

sebanyak 3.14%.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xii

LIST OF FIGURES xiv

LIST OF ABBREVIATIONS xxiv

LIST OF ALGORITHMS xxv

1 INTRODUCTION 1

1.1 Overview 1

1.2 Background of Research 3

1.2.1 Pixel-Based Approach 6

1.2.2 Format-Based Approach 6

1.2.3 Camera-Based Approach 7

1.2.4 Geometric-Based Approach 8

1.3 Problem Statements 9

1.4 Research Goal 11

1.5 Research Objectives 12

1.6 Research Scope 12

1.7 Significance of the Study 13

viii

1.8 Thesis Outline 13

2 LITERATURE REVIEW 15

2.1 Introduction 15

2.2 Digital Video Forgery Detection 16

2.2.1 Source Identification 19

2.2.2 Splicing 22

2.2.3 Copy-Move Forgery 23

2.3 Framework Processing in Video Forgery

Detection 26

2.4 Tampering of Video Content in Passive

Approaches 28

2.4.1 Tampering in Spatial Domain 31

2.4.1.1 Block Level Attack 33

2.4.1.2 Pixel Level Attack 33

2.4.1.3 Object Removal Attack 35

2.4.1.4 Object Addition Attack 35

2.4.1.5 Object Modification Attack 36

2.4.2 Tampering in Temporal Domain 37

2.4.2.1 Frames Addition Attack 39

2.4.2.2 Frames Removal Attack 40

2.4.2.3 Frame Swapping Attack 40

2.4.3 Tampering in Spatial-Temporal Domain 42

2.5 Related Work 45

2.6 Video Compression 51

2.7 Summary 52

3 RESEARCH METHODOLOGY 55

3.1 Introduction 55

3.2 Research Framework 56

3.2.1 Phase A: Problem Formulation 58

3.2.2 Phase B: Design 58

3.2.3 Phase C: Development 59

ix

3.2.4 Phase D: Implementation 59

3.2.5 Phase E: Validation of the Proposed

Methods 62

3.2.5.1 Qualitative Evaluation 63

3.2.5.2 Quantitative Evaluation

Approach

63

3.3 Benchmarking 66

3.4 Datasets in Video Forgery Detection 67

3.4.1 SULFA Dataset 67

3.4.1.1 Video Forgery Process 68

3.4.1.2 Manipulating Digital Video

Forgery 69

3.4.2 The Video Tampering Dataset (VTD) 72

3.4.2.1 Video Tampering Domain 72

3.4.2.2 Framework of Video Forgery

Process 78

3.4.2.3 Ground Truth 80

3.8 Summary 81

4 DETAILED PROPOSED METHODS OF VIDEO

COPY-MOVE FORGERY DETECTION

82

4.1 Introduction 82

4.2 Convert Videos into Image Frames 85

4.3 Computation of Optical Flow 87

4.3.1 Convert RGB Colour Frames to

Greyscale 91

4.3.2 Partition Frame into Non-Overlapping

Blocks 93

4.3.3 Diamond Search Algorithm 96

4.3.4 Slantlet Transform-based Matching

Function 100

4.3.5 Optical Flow Computation for Each

Two Successive Frames 114

x

4.4 Optical Flow in Copy-Move Forgery Detection 118

4.4.1 Detection of Deleted Object in Video

Forgery

119

4.4.2 Cloning Object Detection 127

4.4.2.1 Extraction of Moving Object 132

4.4.2.2 Represent Moving Object 139

4.4.2.3 Compute Similarity Matching

and Forgery Detection 148

4.5 Summary 157

5 RESULTS AND DISCUSSION 158

5.1 Introduction 158

5.2 Performance Evaluation of Deleting Object

Detection 160

5.2.1 Qualitative Evaluation of Deleting

Object Detection 160

5.2.1.1 Experimental Results for

SULFA Dataset 161

5.2.1.2 Experimental Results for

VTD Dataset 167

5.2.2 Quantitative Evaluation of Deleting


5.2.2.1 Frame-based Detection

Evaluation 178

5.2.2.1 Block-based Detection

Evaluation 183

5.3 Performance Evaluation of Cloning Object

Detection 188

5.3.1 Qualitative Evaluation of Cloning


5.3.2 Quantitative Evaluation of Cloning


5.3.2.1 Frame-based Detection 199

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Evaluation

5.3.2.2 Block-based Cloning Object

Detection Evaluation 201

5.4 Benchmarking 204

5.5 Summary 205

6 CONCLUSIONS AND FURTHER OUTLOOK 206

6.1 Contributions 208

6.2 Further Works 212

REFERENCES 213

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LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Multi frequent happenings in spatio-temporal visual

videos, where parameters are chosen to simulate the

real cases 45

2.2 Summary of video forgery detection method and

classifier extracted features with dataset by previous

work 46

3.1 Summary of operational research design for Phase D 60

3.2 Performance Measures 62

3.3 The meaning of statistical measures, TP, TN, FN,

and FP 65

3.4 Current methods for benchmarking 66

4.1 The parameters of the function gi (n) with different

pixels numbers 104

4.2 The parameters of both filters fi(n)and hi(n)with

different number of pixels 106

4.3 Coefficients of fi(n) and hi(n) functionsusing the

Algorithm 4.7 with N=8 107

5.1 Frame-based deleting object detection using TP, TN,

FP, and FN. 179

5.2 Result of Frame-based deleting object detection 181

5.3 Jaccard and Dice scores for the deleting object

localisation 185

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5.4 Evaluation result of cloning object detection using

SULFA Dataset

199

5.5 Jaccard and Dice scores for the cloning object

localisation 201

5.6 The proposed method versus the state-of-the-art

methods in terms of accuracy rate 204

xiv

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Text sub-images (blocks) Example of video Copy-

Move forgery (a) Original video(b) Tampered video

from SULFA dataset 3

1.2 Sample of video copy-move forgery (a) Original

video (b) Tampering video from VTD dataset 4

1.3 Example of video copy-move forgery 10

2.1 Classification of video forgery detection approaches 18

2.2 Forged videos recorded realistic scenes (a)

Wrapped box on a bookshelf (b) Bicycle in front of

a building 21

2.3 Video copy-move tampering (a) Original video (b)

Delete/add object (c) Delete/add sequence of frames

(d) Tampering both copy-move 23

2.4 Example of a forged video copy-move 24

2.5 Example of (a) Original and (b) Forged of video 25

2.6 General forgery detection 27

2.7 (a) Original video; (b) Spatially tampered video;

(c) Temporally tampered video; and (d) Spatio-

temporal tampered video 29

2.8 Number of papers related to digital video passive

approaches 30

2.9 Slice and macro-blocks Structure 32

2.10 An example of (a) Frame Drop, (b) Frame

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Swapping, and (c) Frame Copying where source is

the video sequence presented

37

2.11 An example of temporal domain: (a) Original

frames; (b) Frame Copying; (c) Frame Drop; and

(d) Frame Swapping 41

2.12 Spatio temporal connotation in passive tampering

detection 44

2.13 Arrows show prediction dependencies between

frames 52

3.1 Block diagram of the research design 57

3.2 The Evaluation of localisation of the video forged

block detection using Jaccard (J) coefficient and

Dice (D) coefficient 66

3.3 A block diagram of the general forgery process 69

3.4 Manipulating video; (a) actual Video, (b) Tampered

video and (c) Subtracted ground-truth frame of (a) 70

3.5 Structures of SULFA Datasets 71

3.6 (a) Original video; (b) Spatially tampered video;

(c)Temporally tampered video; and (d) Spatio-

temporal tampered video. 73

3.7 The process of copy-move forgery 75

3.8 Splicing tampering in video frames 76

3.9 An example of temporal domain: (a) Original

frames; (b) Frame Copying; (c) Frame Drop; and

(d) Frame Swapping 77

3.10 Swapping frames (drop and add) 78

3.11 Framework of the video tampering process 79

3.12 Deducted ground-truth frame of figures (a) and (b) 80

4.1 The proposed methodology framework 84

4.2 Example of conversion of video into frames of the

video named: “Forgery camera_demo” of VTD

dataset 86

4.3 Calculation of Oflow between Frame#68 and

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Frame#69 of “Forgery Dahua_HDCVI” video of

VTD dataset

89

4.4 Displacement velocity of a vector movement 90

4.5 Video frame conversion: Group of RGB video

frames and its converted greyscale of “Forgery

white car” video of VTD dataset 93

4.6 Video frame partitioning into non-overlapping

blocks of 8×8 pixels of video 6_(Elephant) of

SULFA Dataset 95

4.7 Full Search algorithms within thirteen crosses show

all possible checking-point positions within the

circle 97

4.8 Two search patterns are employed in the Diamond

Search algorithm; (a) Large diamond search (LDS)

(b) Small diamond search (SDS) 98

4.9 Search window path examples of three cases of

checking-point. (a) The corner point LDS LDS,

(b) The edge point LDS LDS, and (c) The corner

point LDS SDS 99

4.10 Diamond Search for block movement estimation 100

4.11 The SLTBlock matrix operation 110

4.12 An example of a SLTBlock creation 112

4.13 Calculate movement vector of displacement in each

S2F 115

4.14 The 5 × 5 Gaussian smoothing filter 115

4.15 The optical flow in three temporally-consecutive

frames using the proposed method from video

named: “Real man _street” of VTD dataset 117

4.16 X and Y elements values of the displacement

movement of blocks representing the Optical flow

of human movement in a videonamed “Real man

_street” of VTD dataset 118

4.17 Example of (a) Original (b) Forged duplicate region

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of video frames to hide a specific event, extracted

from video 11_(person) of SULFA Dataset

120

4.18 Example of (a) Original (b) forged optical flow

vector plot of video frames to hide a specific event,

extracted from video 11_(person) of SULFA

Dataset 121

4.19 Minimum movements in video frames of SULFA

dataset 123

4.20 A flowchart for detection of deleted of object using

minimum movement in videos frames 125

4.21 Result of the Optical Flow matrix of the frame 138

extracted from 11_ forgery (person) video of

SULFA Dataset 126

4.22 The identical object movement in video named

“02_forgery (red ball)” of SULFA Dataset 129

4.23 Example of (a) Original and (b) Forged optical flow

vector plot of video frames to copy an identical

object movement in video named “2_forgery (red

ball)” of SULFA Dataset 131

4.24 Maximum movements in videos frames of SULFA

dataset 134

4.25 The optical flow matrix of the frame 66 extracted

from “2_forgery (red ball)” video of SULFA dataset 136

4.26 Example of (a) Original and (b) Forged optical flow

vector plot of moving object detection from video

named “2_forgery (red ball)” of SULFA Dataset 138

4.27 8-connectivity to link and label all the movement

values 141

4.28 Example of(a) Forged frames 102 and 103, (b)

Vector plot of optical flow and (c) movement

extraction from video named “4_forgery (red ball)”

of SULFA Dataset 142

4.29 The center point Cp of moving objects from frame

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66 to 72 video named “2_forgery (red ball)” of

SULFA dataset

145

4.30 Example of displacement path of each object

movement in video (a) Blue Object movement

labeled 1from frame 62 to 94, (b) Black Object

movement labeled 2 from frame 123 to 152 and (c)

Red Object movement labeled 3 from frame 223 to

255 of connected moving objects from video named

“2_forgery (red ball)” SULFA Dataset 147

4.31 The difference between Euclidean and DTW

matching similarity measure: (a) Euclidean and (b)

DTW frame 62 to 94 and frame 123 to 152 of

connected object movement from video named

“2_forgery (red ball)” of SULFA Dataset 149

4.32 Initialization of the alignment matrix 151

4.33 (a) to (d) Filling of the alignment matrix 152

4.34 Trackback of the optimal complete alignment: (a)

Alignment matrix with the initial point and (b)

Alignment matrix with the optimal path 153

4.35 Overall process of computing the similarity of two

displacement paths using DTW matching 155

5.1 The general framework to achieving the proposed

objectives 159

5.2 Detection result of deleting object obtained from

frame 153 of “11_forgery (person)” video (a)

Original frame (b) Tampered frame (c) Ground-

truth (d) Optical flow (e) The detection result with

forged area (red) 161


frame 292 of “11_forgery (person)” video (a)



forged area (red)

162

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frame 103 of “13_forgery (person walk)” video (a)




















frame 65 of “Camera_Demo” video (a) Original

frame (b) Tampered frame (c) Ground-truth (d)

Optical flow (e) The detection result with forged

area (red) 167


frame 130 of “Camera_Demo” video (a) Original



area (red)

168


frame 400 of “CCTV_London_Str” video (a)


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forged area (red)

169


frame 416 of “CCTV_London_Str” video (a)





frame 400 of “Clarity_Sample” video (a) Original



area (red) 171


frame 430 of “Clarity_Sample” video (a) Original



area (red) 172


frame 185 of “Dahua_HDCVI” video (a) Original



area (red) 173


frame 1 of “man _street” video (a) Original frame

(b) Tampered frame (c) Ground-truth (d) Optical

flow (e) The detection result with forged area (red)

174




flow (e) The detection result with forged area (red) 175




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flow (e) The detection result with forged area (red)

176


frame 315 of “White Car” video (a) Original frame


flow (e) The detection result with forged area (red) 177

5.19 Performance measures of the proposed detection

method implemented on a series of videos from

both SULFA and VTD datasets 182

5.20 The deleting object blocks detection in video

images using Jaccard coefficient and Dice

coefficient 183

5.21 The Jaccard and Dice coefficient detection for of

each video in SULFA and VTD datasets 186

5.22 Incomplete tampered region localization detected

by the proposed method on SULFA dataset, video

named 11_forgery (person) (a) Detection result

after using the proposed method (b) Ground-truth

(c) Incomplete detected the full frame247 187

5.23 Incomplete tampered region localization detected

by the proposed method on SULFA

dataset,13_forgery (person walk) (a) Detection

result after using the proposed method (b) Ground-

truth (c) Incomplete detected full frame 116

187

5.24 Detection result of cloning object obtained from

“2_forgery” video (a) Frame 67 (b) Tampered

frame 128 (c) Optical flow 67 (d) Optical flow

tampered 128 (e) Ground-truth (f) The detection

result of processing by red colour 190


“3_forgery (red ball)” (a) Frame 59 (b) Tampered



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“4_forgery (red ball)” (a) Frame108 (b) Tampered

















tampered102 (e) Ground-truth (f) The detection

result of processing by red colour

195





result of processing by red colour

196






5.32 Performance results obtained by the proposed

method using a series of videos from SULFA

xxiii

dataset 200

5.33 The Jaccard and Dice coefficients for each video of

SULFA dataset 202

5.34 Examples of incomplete tampered region

localization detected by the proposed method on

SULFA dataset, 2_forgery (red ball) (a) Detected

cloning object region (b) Ground-truth (c)

Incomplete detected frame 130 203

5.35 Examples of incomplete tampered region

localization detected by the proposed method on

SULFA dataset, 7_forgery (red car) (a) Detected

cloning object region (b) Ground-truth (c)

Incomplete detected frame 98 203

xxiv

LIST OF ABBREVIATIONS

GOP - Group of Pictures

SULFA - Surrey University Library for Forensic Analysis

VTD - Video Tampering Dataset

NLF - Noise Level Function

CCN - Circular Correlation Norm

HHT - Hilbert-Huang transform

DCT - Discrete Cosine Transform

DS - Diamond Search

SLT - Slantlet Transform

DTW - Dynamic Time Warping

Th1 - Threshold

Th2 - Threshold

TP - True Positive

TN - True Negative

FN - False Negative

FP - False Positive

S2F - Two Successive Frame

Oflow - optical flow

LDS - Large Diamond Search

SDS - Small Diamond Search

MAD - Mean Absolute Difference

MAE - Mean Absolute Error

DWT - Discrete Wavelet Transform

GS - Gaussian Smoothing

xxv

LIST OF ALGORITHMS

ALGORITHM TITLE PAGE

4.1 Conversion of videos into image frames 86

4.2 Conversion of video frames to greyscale 92

4.3 Frames divided into non-overlapping blocks 95

4.4 Calculation of parameter of gi(n) function 104

4.5 Calculation of parameters of fi(n) and hi(n)

filters 106

4.6 Calculation of coefficients of fi (n)and

hi (n) functions 107

4.7 Creation of SLT filter matrix 108

4.8 Transposed operation of SLTfilter matrix 109

4.9 Calculation of SLT Block matrix 110

4.10 Proposed MAE Block Matching function 113

4.11 Minimum optical flow movement value in

each S2F 123

4.12 Detection of the motionless area in each S2F

using the Th1 127

4.13 Maximum optical flow movement value in

each frame 135

4.14 Detection of the moving object area in S2F

using the Th2 136

4.15 Extraction of connected moving object 141

4.16 Computing displacement path based on center

point method 146

xxvi

4.17 DTW matching alignment process 154

4.18 Similarity measure between two displacement

paths 156

CHAPTER 1

INTRODUCTION

1.1 Overview

In the last of few years, with the advent of digital media such as video, images

and audio through internet, the means and the incentive to create digital forgeries have

also multiplied with it. As a matter of fact, powerful tamper media or editing software

and tools allow the creation of perceptually persuasive digital forgeries techniques.

Evolutions in visual digital video technologies such as digital transmission,

compression, storage, and video-conferencing have supported society in many ways.

Compared with digital image, the tampering of digital video is often more

sophisticated and time-consuming (Richao et al., 2014) although it is becoming easier

with the popularity of video editing tools, such as Video Editor and Adobe Photoshop.

The video processing software is commonly used to delete or incorporate

moving objects and change the forged regions with information garnered from their

neighbouring areas (Su et al., 2015). In this background video authentication refers to

a process that confirms the authenticity of a specific video as captured by camera

through searching and detecting various forensic types as to the tampering method. In

this regard, a video sequence can be modified through several forensic methods like

2

modification, combination or create of new video contents. The aim behind video

manipulation is to tamper, doctor or fake an authentic video. The real videos may be

utilized as sources of their tampered counterparts, and such tampering can be

conducted on a single video source or on many sources (Upadhyay and Singh, 2012).

Video forgeries mainly fall into two types of techniques that can be used for

video tampering detection: active forgeries and passive forgeries. In active forgeries,

(Di Martino and Sessa, 2012; Ram et al., 2009), the tampered region can be extracted

using a pre-embedded information such as watermark and fingerprint. However, this

scheme must have source files to embed the watermark first otherwise the detection

process will fail (Ng et al., 2006).

In passive approaches, techniques can be divided into three categories (C.-S.

Lin and Tsay, 2013) namely, source identification (sensor type of camera such as

noise), splicing techniques (multiple video-based forgery), and copy-move detection

techniques (single video-based forgery).

Source camera identification (C.-C. Hsu et al., 2008; Kang et al., 2012) is a

crucial issue that focuses on many issues that are linked to camera that is concerned

with identifying the source of a digital device; for example, mobile phones,

camcorders, and cameras. On the other hand, in splicing techniques forgery (Wahab

et al., 2014), two videos are combined to create one tampered video or a composite of

two or more videos are combined to create a fake video. Furthermore, splicing

tampering becomes difficult if the directions and lighting conditions are different

during recording with a dynamic camera (He et al., 2012; Y.-F. Hsu and Chang, 2007).

One of the major challenges that are faced by digital forensics is video copy-

move forgery or digital content tampering (Li and Huang, 2014). More recently, there

have been various types of forgery methods developed by hackers, with operation

duplication operation or copy-move on top of the list. In the context of cloning, the

3

main objective is to hide or incorporate an object from the same video scene to develop

a new scene. This process has become widely used as a malicious way of hiding

evidence (Qadir et al., 2012).

(a) (b)

Figure 1.1 Example of video Copy-Move forgery (a) Original video (b) Tampered

video from VTD dataset

Figure 1.1 illustrates an example of video copy-move forgery, where (a) the

original video and (b) the tampered video. For example, the ducks are recorded in the

video twice by taking part of the video (e.g., white car) and pasting it in another region

within the same video. It is challenging to detect this type of video forgery if the copy-

move procedure is carefully and actually carried out. Therefore, it is necessary to have

reliable and efficient methods to detect copy-move forgery for applications in law

enforcement, particularly forensics (Milani et al., 2012).

1.2 Background of Research

Dynamic developments in digital technologies and extensively utilized digital

video recording systems along with sophisticated video editing software, high quality

4

processing tools and algorithms, and low cost accessibility as well as easy to operate

digital multimedia devices have led to the increased video tampering and the challenge

of video authentication.

Video copy-move forgery has been identified as a vital form of forgery as it

utilizes the same video frames sources and destination-apart from this, the video frame

is copied and pasted on another part of the same video. In fact there are more subtle

cases found in the standard dataset video copy-move forgery whereby copied frames

are pasted on several places on the same video (many-to-many). Figure 1.2 illustrates

the above cases.

Figure 1.2 Sample of video copy-move forgery (a) Original video (b) Tampering

video from VTD dataset

In the beginning, the problem of copy-move lies in appointing video

authentication that plays a key role in detecting and determining region duplication,

frame duplication or object duplication of video forgery, and locating the factors that

affect video forgery (Richao, et al., 2014).

5

Pioneering methods depend on intrinsic features such as pixel value and

statistical features as well as video files characteristics. Among the many methods that

are considered for video forgery detection are those that are based on the identification

of acquisition device and detection of whether or not two video clips stem from the

one source. In relation to this, (Kot and Cao, 2013) stated that owing to the statistical

source features sensitive nature towards tampering and modifications, it is suitable to

be used in addressing tampering.

A majority of the previous works (Pathak and Patil, 2014; D'Amiano, et al., 2015;

Bestagini et al., 2013) have used Local Binary Patterns (LBP)-based features to identify

duplicated image regions. Though LBP is effective against distortions, scaling, JPEG

compression, blurring and noise adding, it however becomes ineffective when forged

areas are small. This failure may lead to inadvertent errors in the subsequent important

processes such as detection of moving objects and deleting objects. This is to

emphasise that precise duplicated region detection is utmost important in video copy-

move forgery detection. Failure which will result in low detection accuracy.

Beside the duplicated region, detection of moving objects, which traverse from

frame to frame, is also important. Most of the previous works (Pathak and Patil, 2014;

D'Amiano, et al., 2015; Bestagini et al., 2013) used LESH (Local Energy based Shape

Histogram) along with lexicographical sorting to determine objects’ trajectory similarity. The

method is similar to shape-based image retrieval approach. The method has a good efficiency

in detecting copy-move. However, the efficiency falls as the quality of the video frames

degrades. Video tampering refers to the generation of faked videos by adding, deleting

or altering new video object. It usually consists of detection/tracking, video

manipulation, video in-painting and video layer fusion (Kot and Cao, 2013).

The detection methods in passive blind video copy-move forgeries can be

categorized into four and they are pixel-based approach, format-based approach,

camera-based approach and geometric-based approach (Lin and Tsay, 2014).

6

1.2.1 Pixel-Based Approach

According to (Wang and Farid, 2007), pixel-based approaches make use of

high correlation between authentic and forged areas in video frames for the detection

of copy-paste forgery. The drawback lies in the fact that high correlation between

frames is common in normal videos rendering the method useless if the copied regions

are in use from other video frames. In (G.-S. Lin et al., 2011) proposed a video

detection method known as a coarse-to-fine grained method that uses the variation in

colour histograms of adjacent frames that are similar spatially and temporally – it

makes use of the macro-block based correlation algorithm to identify duplication.

However, their method is not able to determine region forgery.

In a similar study, (Zhang et al., 2009) brought forward a method that uses

ghost shadow artefacts presented by the consistencies in painting in order to detect

forged moving object region. Their approach differentiates static background from

moving foreground through block matching that is sensitive to the noise property

(illumination alterations), but due to its inaccuracy, the tampered region in each frame

cannot be identified by their method.

1.2.2 Format-Based Approach

An appearance of format-based approach in the video makes up part of the

forgery chain-of-evidence. A study of high MPEG is usually initialized with

compression video coding standard since some of the research in MPEG video

forgeries focus on the properties of the frames compression efficiency and how it is

affected when a video is tampered.

7

In this background, (Wang and Farid, 2006), proposed spatio-temporal domain

artefacts of doubly compressed MPEG video frames in type of I-frame that is like a

frame sequence of JPEG compressed image although there is considerable correlation

among frames in GOP (Group of Pictures). In relation to this, the determination of

predictive coded I-frame double compression is akin to double compression detection,

and in the case of GOP, insertion or removal of frames will increase the error of motion

estimation. Their method effectively works in detecting frame manipulation but not

in locating tampered object regions. In (Luo et al., 2008) study, the authors brought

forward a new method using the temporal blocking artefacts patterns to detect whether

deletion or insertion has been done on MPEG video prior to recompression with

different GOP structures. Their approaches are effective in frame-level forgery but

not in region-level tampering and localization.

1.2.3 Camera-Based Approach

The main steps of this approach is extracting different types of fingerprints

based on a set of videos then applying pattern recognition techniques in order to detect

forgery. Some fingerprints recognition, which can be used in these techniques include

noise patterns, lens distortion, and inconsistence-related artifacts (Kancherla and

Mukkamala, 2012).

As aforementioned, another set of video source camera identification method

is based on the extraction and measurement of noise characteristics that stem from

camera sensors. Noise is generally random, unwanted and variation of pixel values in

digital file (e.g., videos and images) that are sensors-generated. According to (Bayram

et al., 2005), the noise patterns are mostly utilized as a part of identification process

source owing to their deterministic properties that stem from CCD sensors.

8

(Kobayashi et al., 2010) proposed photon approach shot noise to detect

tampered level regions by using noise characteristics. Their method exploited the

inconsistencies of photon shot noise caused by various video source cameras to detect

between the original and forgery regions. But, their methods approach can merely

detect forgery regions in static scene videos and not suspicious level regions in videos

captured by a moving camera.

1.2.4 Geometric-Based Approach

Geometric-based approaches make use of measurements of objects in the video

and their positions relative to the moving video camera. Based on (Conotter et al.,

2011) study, a forgery technique approach to detect physically inconsistent

implausible trajectory of objects in video frame sequences was employed. However,

their technique can detect manipulation regions in a video sequence and limits the form

of the video frame (i.e., de-interlaced or interlaced).

The research questions regarding automatic video copy-move forgery

detection that are answered in this thesis are:

1. What different techniques for video forgery copy-move detection have been

proposed to date and where does state-of-the-art methods stand today in terms

of detection rates?

2. Which type of features can be extracted to characterizing the video frames?

3. Can a new video forgery copy-move detection technique, which could achieve

better performances in comparison to existing techniques in terms of detection

rate, be proposed?

9

4. How can the exact location of forged area in video forgery copy-move

detection be located?

1.3 Problem Statement

Malicious manipulations and video tampering without any evidence being left

behind has become very affordable and highly used, due to existence of extremely

powerful editing tools such as Adobe Photoshop and video editing software.

Therefore, there has been a swift augmentation of the digitally altered videos on the

Internet and mainstream media. This tendency depicts grave vulnerabilities as well as

minimizes the reliability of digital video. For such reasons, upcoming techniques in

verification of the authenticity and integrity of digital video have been regarded as

being significant, more so when putting into consideration the videos presented as

news items, as evidence in forensic investigation, such as murder surveillance, or part

of video forensics. From such a perspective, the principal goal in video forensics is to

determine and detect video forgery forensics (Amerini et al., 2013).

There entail a myriad of challenges faced by passive technique of detecting

video forgeries and equally have their constraints and setbacks. One of the fascinating

challenges facing the current scholars and researchers in this field is the reduction of

counterfeit positive rate of such approaches, in establishing effusive automatic system

with the capacity to identify image falsification from a wide perspective of video

formats. Additionally, the system detecting such falsification is made to increase its

dependability, robustness, and competence of operation. The key setback realized in

passive approaches is their requirement for several initial videos to approximate the

internal traces, whereas in capable situations there entails nothing else rather than the

video in query (Chen Moet al., 2008). Additional studies regarding this analysis can

be accessed in (Lanjewar et al., 2014).

10

(c) Forged video

(b) Target part of video

Figure 1.3 Example of video copy-move forgery (Qadir, et al., 2012)

Figure 1.3 shows an example of video copy-move forgery in which a region

from one image is copied and pasted within the same video (Muhammad Ghulam et

al., 2014). For instance, there has been a continuous problem in identifying copy-

move areas that have been rotated and scaled from different angles (Lanjewar et al.,

2014).

Against this backdrop, this research therefore concludes that existing video

copy-move detection methods still suffer many drawbacks, which include, among

others:

i. Their performance is mediocre when it comes to video compression, and

in GOP, where addition or deletion of frame increases the estimation of

motion error (Dong et al., 2012; Li and Huang, 2014; Shanableh, 2013).

Their method’s effectiveness is confined to the detection of frame

tampering and not localization of tampered regions.

+ (a) Original video

11

ii. The presence of homogeneous regions in the tampering video further

complicates the video copy-move forgery detection, which normally

increases the false positive and the accuracy rate is far from satisfactory

(Hyun et al., 2013; Su, et al., 2015; Subramanyam and Emmanuel, 2013).

iii. Notwithstanding the achievements realized by prior studies entailing high

correlations between original and forged regions in copy-move forgery

detection (e.g., Hsu et al., 2008; Thakur, 2013; Wang et al., 2014), high

correlation is known to be common in natural videos and the methods

proposed are not effective if the copied regions are within-frame object

tampering calling for more enhancements.

Thus, the remaining issues and drawbacks of the previous works, which have

been mentioned above, compel the author to pursue the research to seek a new

approach to improve the detection rate of video copy-move forgery. With that in mind,

a specific and focused research goal along with its objectives and scope are articulated

and given in the following sub-sections.

1.4 Research Goal

The study aims to design and develop a new video copy-move forgery

detection scheme with high accuracy based on optical flow methods.

12

1.5 Research Objectives

In order to achieve the goal, this study aims to fulfil the following main

objectives:

i. To develop a new method to trace duplicated blocks from each pair of

successive frames of a video using a two-tier approach comprising

Diamond search and Slantlet transform.

ii. To propose a new method to detect and localise both deleted objects and

moving objects using block displacements.

iii. To propose a new cloning object detection method based on displacement

paths of moving objects using Dynamic Time Warping (DTW) matching

algorithm.

1.6 Research Scope

The scope of this research is limited to the following:

i. Datasets: two sets of datasets namely, SULFA (Surrey University Library

for Forensic Analysis) (Qadir, et al., 2012) and VTD (Video Tampering

Dataset) are employed to evaluate the performance of the proposed scheme.

The former is a standard dataset obtained from

http://sulfa.cs.surrey.ac.uk/forged.php. On the other hand, the latter is a self

-created dataset which can be found at:

https://www.youtube.com/channel/UCZuuu-iyZvPptbIUHT9tMrA.

13

ii. Performance evaluation: This study’s only concern is the accuracy rate,

while the computational complexity is beyond its domain.

iii. Type of forgery: This study only focus on copy-move or copy-paste-move

forgery, while other kinds are out of scope of this study.

1.7 Significance of the Study

It is strongly believed that several applications like video copy-move forgery

detection investigations of digital video for forensic investigation such as in the case

of video surveillance, and presenting video evidence in courts of law need more

advanced detection and authentication techniques to prove the trustworthiness of

digital video. In light of the above mentioned issues, the results of this research are

expected to contribute to what is currently known about video copy-move forgery

detection. Nonetheless, the significance of this study is not only limited to forgery

detection, but also to the development of a new method that can be used in the future

in many applications in the field of computer vision.

1.8 Thesis Outline

The organization of this thesis is given in this section. The rest of the chapters

in this thesis begin with brief sections that highlight the aims of each section of the

chapter, and sums up with a short conclusion. Chapter 1, provides an overview of the

research problem and a brief background. The objectives of the research are also

described in this chapter.

14

In Chapter 2, an in-depth review of the existing literature on authentication of

video digital processing on the whole, as well as passive methods in attaining the

study’s objective, specifically are presented. The currently employed approaches and

criteria within the context of counterfeit digital video recognition are highly defined in

this chapter. A review of a current study is conducted inclusive of recent techniques

and methods employed in sensing video forgery tampering. This study area is

moderately novel hence the meagre sources relative to the study topic. Therefore, the

reviewed and availed approaches relate to extensive processing of digital videos, but

only somewhat related to forgery detection in videos forensic. All the chapters are

independent but a flow and coherency of ideas throughout the entire thesis are ensured.

Chapter 3, presents a clear roadmap of this study to guide the reader to achieve

a quick grasp of the detailed research framework. The advantages of using the popular

dataset in the newly developed methods are emphasised. The layout of the entire

research framework, strategies, and procedures are highlighted. This is followed by

Chapter 4, where a detailed design of the proposed method is provided along with the

step-by-step processes and the proposed algorithms employed in it. The chapter also

provides a discussion of the proposed methods entailing the proposed method’s

implementation on video copy-move forgery detection.

The next chapter (Chapter 5), provides the results of the proposed method used

on two datasets SULFA and the VTD dataset of video copy-move forgery detection,

along with the experimental results, detailed analyses, and discussion. It explains the

qualitative and quantitative measurements that are carried out for the performance

evaluations and implementation of the method for every phase with the inclusion of

the detection of the tampered videos and localisation of the forgery region. The

qualitative measurements are based on visual human inspection, while the quantitative

measurements are performed using standard approaches. Lastly, Chapter 6 concludes

the study by emphasizing the major contributions, enumerating the major

achievements achieved and providing recommendations for future studies.

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Table of Corrections

Required Corrections Page No.

The Amendment Made Page No.

Title Video Copy-Move Forgery Detection Scheme

Based On Displacement Paths

Abstract Amended

Chapter 1

Typographical errors including English

language and UTM Formatting

Checked and amended 2, 9 and

11

Background of Research 4 Added two paragraphs about motivation of

previous works 5

Problem statement 8 Added a paragraph about motivation of this

study 9

Figure 1.3 adding (a), (b) and (c)

10 Added figure 1.3 the link motivation of this

study 10

Adding reference at paragraph 1 line 2

10 Added (Muhammad Ghulam et al., 2014) 10

Adding paragraphs about motivation of this study

10 Added two paragraph in page 10 to link a

motivation of Problem Statement 11

Chapter 2



Checked and amended 16, 26, 37 and

51

Give critical analysis (short coming, advantage

of other works) 22

Critical analyses are given in four separate pages

24, 25, 32 and

50

Chapter 3



Checked and amended 59 and

62

Research framework should be contributions

and link to objective 59

Research framework is amended to include the contributions and link to objectives

60 and 61

Adding words 78 Add “see in Figure 3.11” words 78

Chapter 4



Checked and amended 89, 94,

98, 100, and 106

Chapter title need to be changed

81 Changed to “ PROPOSED VIDEO COPY-MOVE

FORGERY DETECTION SCHEME” 82

Need to tie up with the research problem

81 Added paragraph about the proposed

method’s main components and link with research problem and objectives

82

Insert small caption in Fig. 4.1

83 Changed caption of Figure 4.1: “Proposed video

copy-move forgery detection scheme” 84

Chapter 5



Checked and amended 160,193, 194, 195 and 196

Indicating the figure with a symbol in the relevant

figures and tables 180

Each video in Table 5.2 indicated to the name of each video in both datasets SULA and VTD

181



Each video in Table 5.3 indicated to the name of each video in both datasets SULA and VTD

185

Changing words in Figure 5.28

193 Changing words from “ (red ball) “ to

“(Elephant) “ words in Figure 5.28 194


194 Changing words from “ (red ball) “ to “(red

car)“ words in Figure 5.29 195


195 Changing words from “ (red ball) “ to “(white

ball) “ words in Figure 5.30 196


196 Changing words from “ (red ball) “ to “(white

car)“ words in Figure 5.31 197



Each video in Table 5.4 indicated to the name of each video in dataset SULA

199



Each video in Table 5.5 indicated to the name of each video in dataset SULA

201

Chapter 6



Checked

second paragraph is a revisit to problem

background, objectives 205

A new paragraph (2nd. Paragraph) is added to address the issue

206

References

Reference has been deleted form References

212

Al-Sanjary, O. I., Ahmed, A. A. and Sulong, G. (2016). Development of a video tampering dataset for forensic investigation. Forensic

Science International, 266, 565-572.

213

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