Copy Move Forgery Detection Using Key-Points Structure

Transcription

1 Copy Move Forgery Detection Using Key-Points Structure A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Cyber Security by Vinod Parihar 14/MS/030 Under the Supervision of 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) COMPUTER SCIENCE AND ENGINEERING DEPARTMENT SARDAR PATEL UNIVERSITY OF POLICE, SECURITY AND CRIMINAL JUSTICE JODHPUR , INDIA May, 2016

2 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 University 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 In case this undertaking is found incorrect, I accept that my degree may be unconditionally withdrawn. May, 2016 Jodhpur (Vinod Parihar) ii

3 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 supervision 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

4 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 Chancellor and 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

5 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

6 Biographical Sketch Vinod Parihar Inside 3rd Pole Mahamandir, Jodhpur,Rajasthan PIN Mob. No 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, B.Tech. in Computer Science and Engineering from place with 68% in Intermediate from Shri Sumer S. S. School, Jodhpur with 68% in High School from D.B.V.K.Sec.School, Jodhpur, with 66% in vi

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

8 sults. Insanity is doing the same thing, over and over again, but expecting different re- -Albert Einstein viii

9 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, rotation 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

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11 Contents Acknowledgment Biographical Sketch Synopsis iv vi ix 1 Introduction Overview Image Forensics Various techniques for tempering Image: Various Techniques in Image Forgery Detection: Organization of Thesis Literature Survey Introduction Block Diagram of Process of CMFD Classification of CMFD methods Block-based algorithms Keypoints-based algorithms Problem Statement xi

12 3 CMFD using Key-Points Structure : Proposed Method Introduction Proposed Method: Introduction Steps of CMFD using Key-Points Structure: Reference Method: CMFD by matching triangles of key-points: Introduction Mean Vertex Descriptors based Triangle Matching method: Dataset of CMFD by Matching Triangles of key-points: Result Evalution Metric: Implementation of Proposed method: Graph Analysis: Summery: Texture based CMFD Method : Proposed Method Introduction Overview of localized angular phase method: Proposed Method: Dataset : Result Evalution Metric: Implementation of Proposed method: Result testing Through graph analysis of Proposed Method based on Texture : Conclusion and Future Work 36 References 39 xii

13 List of Figures 1 Example for Copy-Move Forgery 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 3 Example for Image Retouching Forgery( Leftside original image and rightside forge image.) Image Forgery classification Block diagram of Detection of Copy-Move forgery Classification of CMFD methods Desktop Application of Proposed method with circle points output image of Proposed method another output image of Proposed method Main form of Desktop application of Reference method Sift Angle Method Sift Vertex Method Surf Angle Method Surf Vertex Method Comparative Result of Both method on Dataset Comparative Result of Both method on Dataset xiv

14 17 Comparative Result of Both method on Dataset LAP feature in a block Super LAP feature in a block Input Image into Desktop Application LAP points on Image Output Image into Desktop Application Output of another Image into Desktop Application Result of Proposed method on Dataset [3], [4] and [20] xv

15 List of Tables 1 Frequency based CMFD methods Intensity based CMFD methods Moments based CMFD methods Dimensionality based CMFD methods Keypoints-based CMFD methods Metric for Testing Result of Proposed methods Technology used for implementation of Proposed methods Metric for Testing Result of Proposed methods Technology used for implementation of Proposed methods xvi

16 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

17 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 methods 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

18 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

19 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

20 1.5 Organization of Thesis Figure 4: Image Forgery classification. The work did has been condensed in five chapters. Chapter 1 describes the image forensic, various techniques of image forensics and different 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 details 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

21 Chapter 2 Literature 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

22 Block diagram of process of CMFD is shown in figure 1. Block diagram shows common 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 feature 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

23 Figure 6: Classification of CMFD methods 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 component 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 divided into 4 groups as follows[1] (a) Frequency based: various frequency transform methods are available for transform. Various CMFD methods of this group is listed as follows 8

24 Table 1: Frequency based CMFD methods Name of Methods Feature lenths DCT 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 methods Name of Methods Feature lenths Luo 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

25 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 features 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 methods Name of Methods Feature lenths Zernike 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 transformation 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 methods Name of Methods Feature lenths PCA - 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

26 on small sub-block of the image and lexicographically sorting is used for detecting facsimile regions on the image. This method is invariant with noise and jpeg compression 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 methods Name of Methods Feature lenths SIFT 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 segmentation 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

27 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 Geometric 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

28 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 methods 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 image. 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). Postprocessing 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

29 operation like compression, blurring, color change, brightness change and contrast adjustment produce a different impact on CMFD process. 3.2 Proposed Method: 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 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. V m i j V m i k T H v (1) 14

30 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: Introduction E. Ardizzone et al. [3] proposed a novel approach which is based on key-points structure 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 detector 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 descriptor. 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. Therefore 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

31 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 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). V m i = (V 1 i + V 2 i + V 3 i)/3 (2) Where Vj = 1.3 i=1, 2, 3 are the descriptor value of three vertex of triangle. V m i 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

32 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: V m i V m j T H v (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 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

33 3.4 Result Evalution Metric: For find out Accuracy of Proposed methods, we are used following parameter as follow: Serial Number Table 6: Metric for Testing Result of Proposed methods Parameter Formulas Description of Parameter Name 1. True Positive Rate (TPR) 2. True Negative Rate (TNR) (TP)/(TP+FN) (TN)/(TN+FP) here TP stands for True Positive or Forge part identify as forge part on Tampered image FN stands for False Negative or Forge part does not identify as forge part on Tampered image here TN stands for True Negative or Original part identify as forge part on Tampered image FP stands for False Positive or Original part identify as forge part on Tampered image 3. Accuracy (TP+TN)/(TN+FP+TP+FN) Accuracy define method accuracy in numeric form. 4. False Negative FN/(FP+TN) this is represent rate of false matching or Rate worng output. (FNR) 5. False Positive FP/(FP+TP) this is represent rate of true matching or Rate correct output (FPR) Implementation of Proposed method: Technology used: For Implementation of this proposed method, we used Python Language with Opencv. there are list of component of implementation as follow: 18

34 Table 7: Technology used for implementation of Proposed methods Serial component name Description of Component Number 1. Operating System we used Ubuntu OS in which python programming is easier than Window OS. 2. Programming Language Python Modul of Python Open cv2, scipy, matplotlib.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

35 Figure 8: output image of Proposed method Figure 9: another output image of Proposed method 20

36 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

37 Figure 11: Sift Angle Method 3. Sift Vertex Method Frame: Figure 12: Sift Vertex Method 22

38 4. Surf Angle Method Frame: Figure 13: Surf Angle Method 5. Surf Vertex Method Frame: 23

39 Figure 14: Surf Vertex Method Graph Analysis: Figure 15: Comparative Result of Both method on Dataset 0 24

40 Figure 16: Comparative Result of Both method on Dataset 1 Figure 17: Comparative Result of Both method on Dataset 2 25

41 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 complex 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. therefore 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 number 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

42 Chapter 4 Texture 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

43 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,..., 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: 1, ifc k 0 b(k) = 0, otherwise (9) Where b(k) is the sign of each coefficient. 28

44 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 b(k)2 k 1 (10) k 1 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) S L AP i = M Li (12) 29

45 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: , 46: 61154, 40: 14978, 63: 5765, 44: 5029, 42: 1722, 32: 1615, 43: 77, 60: 5 There are total 30

46 nine group of similar feature. Total pixels have Super LAP value 47. Total pixels have Super LAP value 46 and so on. In 1(b), white pixels LAP value is 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 S L AP j i S L AP k i = 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 images which contains geometric 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

47 4.5 Result Evalution Metric: For find out Accuracy of Proposed methods, we are used following parameter as follow [23]: Serial Number Table 8: Metric for Testing Result of Proposed methods Parameter Formulas Description of Parameter Name 1. True Positive Rate (TPR) 2. True Negative Rate (TNR) (TP)/(TP+FN) (TN)/(TN+FP) here TP stands for True Positive or Forge part identify as forge part on Tampered image FN stands for False Negative or Forge part does not identify as forge part on Tampered image here TN stands for True Negative or Original part identify as forge part on Tampered image FP stands for False Positive or Original part identify as forge part on Tampered image 3. Accuracy (TP+TN)/(TN+FP+TP+FN) Accuracy define method accuracy in numeric form. 4. False Negative FN/(FP+TN) this is represent rate of false matching or Rate worng output. (FNR) 5. False Positive FP/(FP+TP) this is represent rate of true matching or Rate correct output (FPR) Implementation of Proposed method: Technology used: For Implementation of this proposed method, we used Python Language with Opencv. there are list of component of implementation as follow: 32

48 Table 9: Technology used for implementation of Proposed methods Serial component Description of Component Number name 1. Operating System we used Ubuntu OS in which python programming is easier than Window OS. 2. Programming Python 2.7 Language 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

49 Figure 21: LAP points on Image Figure 22: Output Image into Desktop Application 34

50 Figure 23: Output of another Image into Desktop Application Result testing Through graph analysis of Proposed Method based on Texture : Figure 24: Result of Proposed method on Dataset [3], [4] and [20] 35

51 Chapter 5 Conclusion and Future Work Copy-Move Forgery Detection (CMFD) methods work for detecting all types of copymove 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

52 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 postoperation 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

53 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,

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