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Copy & Move Image Forgery Detection using 2D-DWT based Hybrid Algorithm

Electronics & Instrumentation Engineering Department Page 1

1. Abstract

As society has become increasingly depend upon digital images to communicate visual information. Image would provide better impact in convincing someone of something rather than pure description by word. Nowadays one of the principal means for communication is digital visual media. Digital image widely used in various field like medical imaging, journalism, scientific manipulation and digital forensics. Digital image forgery creates more problems on real world. In most digital image communication the main problem is its authenticity. Digital image forensics is an ongoing new research field which aims at finding the authenticity of images by recovering information. There are several different tampering attacks but, surely, one of the most common and immediate one is copy-move. With the advent of powerful image editing tools, manipulating images and changing their content is becoming a trivial task. It is now possible to add, modify, or remove important features from an image without leaving any perceptual traces of tampering. With more than several million

pictures uploaded daily to the net, and the introduction of e-Government services, it is becoming important to develop robust detection methods to identify image tampering operations. To this end, image forensics techniques aim at restoring trust and acceptance in digital media by uncovering tampering methods. Such detection techniques are the focus of this research.



2. Introduction A digital image is a numeric representation of a two-dimensional image. Depending on whether the image resolution is fixed, it may be of vector or raster type. Without qualifications, the term "digital image" usually refers to raster images also called bitmap images. When we see a picture on our monitor or use our digital camera (or scanner), the image we are viewing or dealing with is not continuous like a pencil drawing it is made up of many small elements ( known as pixel) next to each other. When we have enough elements, we get the illusion of a picture or image.

Early digital images (before color) appeared in black and white. The tiny elements that comprised digital images were either black or white. These two colors corresponded to 1 and 0 (called BITS or Binary digits). Digits 1 and 0 are used in the binary (base 2) system. Thus, a map (pattern) made up of these 1s and 0s was referred to as a bit-map. All digital images are a rectangle or square.

We are living in an era of digital revolution which made it very easy to access, process, and share information. Such a technological advance, however, brought with it major security challenges. With the increased growth of technology and powerful algorithms for manipulating images including software like Photoshop, Corel Draw, and others, it is becoming very difficult to discriminate between an authentic picture and its manipulated or doctored version. Image forgery is becoming indeed a challenge for individuals as well as for institutions. The basic concept of image forgery is the digital manipulation of pictures with the aim of distorting some information in these images. The manipulation of images using computer techniques is not new and gained a lot of popularity and even acceptance in diverse areas such as forensic investigation, IT, intelligence services, medical imaging, journalism, etc. The move towards paperless workplaces and the introduction of e-Government services meant more data stored in digital format and more challenges to securing authentic data. Unfortunately, documents, files, voice data, and image data are all vulnerable to manipulation and doctoring. Such a challenge triggered a wide interest among researchers in developing robust techniques for detecting forged images. To this end, image forensics, in particular, is at the forefront of security applications aiming at restoring trust

and acceptance in digital media by uncovering counterfeiting methods.



3. Literature Review [1] S. A. Fattah1 et.al [1] has proposed a scheme for copy-move forgery detection in digital

images based on 2D-DWT with the help of block matching procedure. The proposed candidate block selection algorithm helps in avoiding the huge computational burden involved in block matching operation among all blocks. Moreover, in DWT coefficient matching, instead of using both approximate and detail coefficients, only the former type is employed. In the second stage, as the candidate blocks are matched with all overlapping blocks, high level of detection accuracy is achieved both in case of single and multiple copy-move forgeries. it is observed that the proposed scheme offers very satisfactory performance in terms of hit rate, miss rate, and false detection rate.

[2] M.Sridevi et.al [2] has performed and analyzed copy-move image forgery techniques and proposed a parallel block matching algorithm to detect the forged regions, if copy and paste are done in the digital image. The simulation results show that the proposed parallel algorithms reduce the execution time. The false detection rate enables us to decide correct block size for accurate detection. Usage of the parallel environment has drastically reduced time complexity of the algorithm.

[3] Nathalie Diane Wandji et.al has proposed a method to detect copy-move forgery in digital images based on DCT algorithm. Firstly, the color image is converted from RGB color space to YCbCr color space and then the R, G, B and Y-component are splitted into fixed-size overlapping blocks and, features are extracted from the R, G and B-components image blocks on one hand and on the other, from the DCT representation of the R, G, B and Ycomponent image block. The feature vectors obtained are then lexicographically sorted to make similar image blocks neighbors and duplicated image blocks are identified using Euclidean distance as similarity criterion. Experimental results showed that the proposed method can detect the duplicated regions when there is more than one copy move forged area in the image and even in case of slight rotations, JPEG compression, shift, scale, blur and noise addition.

[4] Li Jing, and Chao Shao was explained firstly analyzes and summarizes block matching technique, then introduces a copy-move forgery detecting method based on local invariant feature matching. It locates copied and pasted regions by matching feature points. It detects feature points and extracts local feature using Scale Invariant Transform algorithm. Matching



local features is based on k-d tree and Best-Bin-First method. Through analysis we learn computational complexity of the proposed method is similar to existing block-matching methods, but has better locating accuracy. Experiments show that this method can detect copied and pasted regions successively, even when these regions are operated by some process, such as JPEG compression, Gaussian blurring, rotation and scale.

[5] M. Ali Qureshi and M.Deriche has given Image forensics techniques aim at restoring trust and acceptance in digital media by uncovering tampering methods. Such detection techniques are the focus of this paper. In particular, we provide a survey of different forging detection techniques with a focus on copy and move approaches. Our brief overview of copy-move image forgery detection techniques shows that this area of research is still in its flourishing stage, and holds a huge potential for future R&D applications. Although many of the techniques discussed in this paper require some types of assumptions to provide excellent detection results, with more research efforts, we expect some robust methods to become standard tools in the near future. Our survey also showed that there is an urgcvvcent need to build a large database for testing image forgery detection algorithms for researchers to conduct experiments and benchmark their results.

[6] Ghulam Muhammad1et.al has presented an image forgery detection method based on Gabor filters and discrete cosine transform (DCT) is proposed. The output of this method is to determine whether an image is authentic or forged. In this method, first, the input image is converted to gray scale image. Second, several Gabor filters with different scales and orientations are applied to the image. Then the DCT from all the filter outputs (subbands) is calculated. The first N coefficients of the DCT from all the subbands are concatenated to form the feature vector. Some feature selections are used to find optimal feature set. Support vector machine (SVM) is used as a classifier. In the experiments, the proposed method outperforms some state-of-the-art methods of image forgery detection. Different color components such as chrominance, luminance, and gray are evaluated. The proposed method is tested on CASIA v1.0 and v2.0 databases.



4. Research Objectives In this research work an efficient, robust and hybrid copy-move image forgery detection Algorithm will be developed based on a block matching algorithm. Instead of considering spatial blocks, 2D-DWT, DCT and SIFT algorithm will be used for hybrid algorithm. These algorithms will be performed on the forged image and then DWT domain blocks are considered, where only approximate DWT coefficients are utilized. DCT or SIFT will be used to measure the feature of the image. To reduce the computational time and increase the efficiency of the algorithm instead of block matching among the all block some reference blocks are first selected from the non-overlapping blocks. These non-overlapping blocks are compared with the reference block. A similarity criterion is introduced to finally detect the forged blocks. Extensive simulation is carried out on several forged images to verify and efficiency of the proposed algorithm.

The main objectives of the research are as follows: 1. To develop an efficient, robust and hybrid technique for copy-move image forgery detection

algorithm.

2. To evaluate the efficiency of the developed algorithm using some of the available standard image datasets.



5. Types of Digital Image Forgery Image forgery or tampering is defined as adding, changing, or deleting some important features from an image without leaving any obvious trace. There have been various methods used for forging an image. Based on the techniques used to create forged images, There are different ways by which image can be forged. Digital image forgery can be divided into three main categories.

1. Copy-Move Forgery

2. Image Forgery using Splicing 3. Image Re-sampling

Some common image manipulation with the intension of deceiving a viewer includes:-

1. Copy and paste 2. Composition or Splicing 3. Retouching, healing, cloning

4. Content embedding or steganography

5.1 Copy Move Forgery In copy-move forgery (or cloning), part of the image of any size and shape is copied and pasted to another location in the same image to hide some important information or to duplicate portions of the image. As the copied part came from the same image, its important properties such as noise, color and texture do not change and make the detection process difficult. The authenticity of the image is doubted when duplication of objects or regions occurs in the image. Since the copied segments come from the same image, its properties will be compatible with the rest of the image, thus it is very difficult for a human eye to detect such type of forgery. Fig. 1 and Fig. 2 depict the copy move image forgery.



Fig. 5.1 Original Image

Fig. 5.2 Forged Image .



5.2 Image Forgery using Splicing Image splicing is a common form of digital image manipulation or image forgery. It is one such type of tampering; Image splicing is also called as image composition. In image splicing, fragments from two or more images are combined to create a new image. Image splicing uses cut-and-paste techniques from one or more images to produce a new fake image. When splicing is performed carefully, the borders between the spliced regions can visually be imperceptible. Splicing, however, disturbs the high order Fourier statistics such as the bi-spectrum; these statistics can subsequently be used in detecting forgery. Fig. 3 shows the basic principle of image splicing. Fig. 4 shows a nice example of image splicing in which the images of the shark and the helicopter are merged into one image.

Fig. 5.3 Image Forgery using Splicing

Fig. 5.4 Example of Image Splicing



5.3 Image Resampling To create a high quality forged image, some selected regions have to undergo geometric transformations like rotation, scaling, stretching, skewing, flipping etc. The interpolation step

plays a central role in the resampling process and introduces non-negligible statistical changes. Resampling introduces specific periodic correlations into the image. These correlations can be used to detect forgery caused by resampling. In Fig. 5 the image on the left is the original scene while the one on the right is the forged image obtained by rotation and scaling it.

Fig. 5.5 Example of Image Resampling



6. Image Forgery Detection Techniques Given the advanced algorithms used in forging, determining the authenticity and integrity of digital images is becoming a real challenge to the naked eyes as well as to machines. It is becoming very important to develop robust detection methods to identify image tampering operations. Image authentication solution is classified into two types.

6.1 Intrusive (Active) Forgery Detection In active methods, a digital watermark or a digital signature is embedded inside the original image uses a known authentication code embedded into the image content before the images are sent through an unreliable public channel. Which can later be used to prove or reject the authenticity of the image? But these methods have strong limitations in a sense that the watermark must be embedded either by the acquisition device (camera), or by an authorized person processing the image. This is an impractical approach for most situations.

6.2 Non-Intrusive (Passive) or Blind Forgery Detection In passive or blind approaches, no prior information about the source image is required. These methods use the fact that the forging operation results in statistical changes in the digital images or there are at least some marks left by the camera during the creation process, and these can be used to detect the tampering attack. The blind image forensics detection methods that are currently in use can be grouped into six broad categories i.e. pixel-based, format-based, camera based, source camera identification-based, physics-based and geometric-based. We display in Fig. 6 the tree diagram of different blind image forensics detection methods that are found in the literature. Blind passive forgery detection methods are broadly categorized as being (a) visual and (b) statistical. Visual methods are based on visual clues that may not require any hardware or software tools. In contrast, the statistical methods are considered more robust and convincing as they analyze the pixel values of the image. The operations that are performed in Passive or blind image forensics has three main aspects:



Fig. 6.1 Blind Image Forensics Detection Methods

1. Source Identification, 2. Forgery Detection

3. Detection of Computer Generated Images. 4. Detection of copy/move forgery

Source identification specifies the source device that had been used to capture the image, whereas forgery detection traces the tampering evidence. The availability of traces or clues would indicate that the image in question is tampered with or otherwise. Owing to the availability of sophisticated software and hardware tools, it is possible to create computer generated images and illusions. The film industry has been actively using such tools to routinely turn fiction into reality with real-life accuracy.



7. Detection of copy/move forgery Copy/move forgery is one of the most popular forms of tampering in which some region is

copied from a particular location in an image and thereafter pasted at one or more locations within the same image or a different image of preferably the same scene. Example is given in Fig. 7 to demonstrate the copy/move forgery. The original image, as reported in the form of Fig. 7a, is depicting two army vehicles. The image is forged to obtain the image in Fig. 7b. The truck has been camouflaged from the image by copying a region that is roughly of a circumference as indicated by the circle and moved to the location of the truck (in the original image). Copy/move forgery detection can be found in. A simple taxonomy of such methods presented in most of these publications follow the classification. Broadly speaking, the detection methods may either be brute force, involving exhaustive search or block based. These techniques usually rely on the correlation between the original patch and the suspected pasted version. The brute force approach involves an exhaustive search that overlays a given image with circularly shifted versions to examine matching segments. Exhaustive search is not that effective when some

post-processing is applied to the copied area. Moreover, the computational complexity is too high to make such a comparison with an attractive proposition. With very large copy/move

patches, autocorrelation may also be an effective strategy to reduce the complexity. However, normally the patch sizes cannot be made to conform to about a quarter of the forged image. Block-based matching techniques give better results in comparison with the exhaustive search- and autocorrelation-based methodologies. Exact matching of blocks may have limited value in the face of the fact that usually the forged areas are post-processed and may not retain the original values. Approximate block matching can be a better option as one can impose some threshold on the (mis)matching or extract robust features (RFs) from the suspect area for comparison. A typical approximate block matching strategy splits the image into overlapping blocks and applies a suitable technique to extract features on the basis of which the blocks are compared to determine similarity.



Fig. 7.1 Example of Copy-Move forgery (a) Original image, (b) Tampered Image

7.1 Forgery Detection Methods

Forgery detection methods become much more complicated to deal with the latest forgery techniques. This back to the availability of digital editing tools, alteration, and manipulation become very easy and as a result forgery detection becomes a complex and threatening problem. Image forgery detection can be manipulated in various ways with many simple operations like affine transforms such as translation, scaling, etc., compensation operations such as brightness, colors, contrast adjustments, etc., suppression operation such as noise extraction, filtering, compression, etc.

The forgery detection method of copy-move forgery detection has following main parts.

1. Discrete Wavelet Transform

2. Lexicographic Sorting 3. Shift Vector Calculation

4. Neighbor block matching

7.1.1 The Continuous Wavelet Transform and the Wavelet Series

The Continuous Wavelet Transform (CWT) is provided by equation 7.1, where x (t) is the signal to be analyzed. (t) is the mother wavelet or the basis function. All the wavelet functions used in the transformation are derived from the mother wavelet through translation (shifting) and scaling (dilation or compression).

(,s) = (,s) = (,s) = (,s) =

|| ().* (.* (.* (.* (

) dt) dt) dt) dt 7777.1.1.1.1



The mother wavelet used to generate all the basis functions is designed based on some desired characteristics associated with that function. The translation parameter relates to the location of the wavelet function as it is shifted through the signal. Thus, it corresponds to the time information in the Wavelet Transform. The scale parameter s is defined as |1/ frequency| and corresponds to frequency information. Scaling either dilates (expands) or compresses a signal. Large scales (low frequencies) dilate the signal and provide detailed information hidden in the signal, while small scales (high frequencies) compress the signal and provide global information about the signal. Notice that the Wavelet Transform merely performs the convolution operation of the signal and the basis function. The above analysis becomes very useful as in most practical applications, high frequencies (low scales) do not last for a long duration, but instead, appear as short bursts, while low frequencies (high scales) usually last for entire duration of the signal.

The Wavelet Series is obtained by discretizing CWT. This aids in computation of CWT using computers and is obtained by sampling the time-scale plane. The sampling rate can be changed accordingly with scale change without violating the Nyquist criterion. Nyquist criterion states that, the minimum sampling rate that allows reconstruction of the original signal is 2 radians. Where is the highest frequency in the signal. Therefore, as the scale goes higher (lower frequencies), the sampling rate can be decreased thus reducing the number of computations.

7.1.2 Discrete Wavelet Transform

The Wavelet Series is just a sampled version of CWT and its computation may consume significant amount of time and resources, depending on the resolution required. The Discrete Wavelet Transform (DWT), which is based on sub-band coding, is found to yield a fast computation of Wavelet Transform. It is easy to implement and reduces the computation time and resources required.

In CWT, the signals are analyzed using a set of basis functions which relate to each other by simple scaling and translation. In the case of DWT, a time-scale representation of the digital signal is obtained using digital filtering techniques. The signal to be analyzed is passed through filters with different cutoff frequencies at different scales.

The DWT provides a time-frequency representation of the signal. It uses multi-resolution technique by which different frequencies are analyzed with different resolutions. Because of its



innate multi-resolution uniqueness, the DWT is employed in the proposed method. The continuous wavelet transform of a signal s(t) using a mother wavelet (t) is mathematically defined in equation 7.2.

(, )==== ()

!7.27.27.27.2

Where a and b represent scale and shift parameters, respectively. The DWT coefficients are obtained by restricting the scale a to powers of 2 and the position b to integer multiples of the scales, and are given in equation 7.3 and 7.4.

#$,% = &$& '(())(&$ %)+( 7.3

Where j and k are integers and j,k are orthogonal baby wavelets defined as

)$,% = &$&)(&$ %) 7.4

One advantage of using the DWT with decimation is that the size of the transformed data will be reduced to one half at each level. Which results in four decomposed sub images: LL, LH, HL, and HH? The HL band indicates the variation along Xaxis while the LH band indicates the Y-axis variation. The power is more compact in the LL band that corresponds to the approximation image and this LL band image is used in the proposed scheme.



8. Discrete Wavelet Transform Operation

In the discrete wavelet transform, a signal (image) can be analyzed by passing it through an analysis filter bank. This analysis filter bank, which consists of a low pass and a high pass filter at each decomposition stage, is commonly used in image processing.

When a signal passes through these filters, it is split into two bands. The low pass filter, which corresponds to an averaging operation, extracts the coarse information of the signal. The high pass filter, which corresponds to a differencing operation, extracts the detail information of the signal.

A two-dimensional transform (see Fig. 11.1) can be accomplished by performing two separate one-dimensional transforms. First, the image is filtered along the x dimension. Then, it is followed by filtering the sub-image along the y-dimension. Finally, we have split the image into four bands denoted by LL, HL, LH and HH after one-level decomposition (see Fig. 11.1 b).

Figure 8.1: Two-dimensional Discrete Wavelet Transform: a) Original image, b) One level decomposition, c) Two levels decomposition



The two-dimensional extension of DWT is essential for transformation of two-dimensional signals, such as a digital image. A two-dimensional digital signal can be represented by a two-dimensional array X [M, N] with M rows and N columns, where M and N are non negative integers. The simple approach for two-dimensional implementation of the DWT is to perform the one-dimensional DWT row-wise to produce an intermediate result and then perform the same one-dimensional DWT column-wise on this intermediate result to produce the final result. This is shown in Figure 11.1(b). This is possible because the two-dimensional scaling functions can be expressed as separable functions which is the product of two-dimensional scaling function such

as,-(., /) = ,0(.),0(/). The same is true for the wavelet function (x, y) as well. Applying the one-dimensional transform in each row, two sub bands are produced in each row. When the low-frequency sub bands of all the rows (L) are put together, it looks like a thin version (of size 1 3- ) of the input signal as shown in Fig. 4.2(b). Similarly put together the high-

frequency sub bands of all the rows to produce the H sub bands of size M 3- , which contains mainly the high-frequency information around discontinuities (edges in an image) in the input signal. Then a one-dimensional DWT column-wise on these L and H sub bands (intermediate result), four sub bands LL, LH, HL, and HH of size 4-

3- are generated as shown in Fig. 11.2(d).

LL is a coarser version of the original input signal. LH, HL, and HH are the high frequency sub bands containing the detail information. It is also possible to apply one-dimensional DWT column-wise first and then row-wise to achieve the same result. Fig. 11.2 comprehends the idea describe above.

The multiresolution decomposition approach in the two-dimensional signal is demonstrated in Fig. 11.2(e). After the first level of decomposition, it generates four sub bands LL1, HL1, LH1, and HH1 as shown in Fig. 11.2(d). Considering the input signal is an image, the LL1 sub bands can be considered as a 2:1 sub sampled (both horizontally and vertically) version of image. The other three sub bands HL1, LH1, and HH1 contain higher frequency detail information. These spatially oriented (horizontal, vertical or diagonal) sub bands mostly contain information of local discontinuities in the image and the bulk of the energy in each of these three sub bands is concentrated in the vicinity of areas corresponding to edge activities in the original image. Since LL1 is a coarser approximation of the input, it has similar spatial and statistical characteristics to



the original image. As a result, it can be further decomposed into four sub bands LL2, LH2, HL2 and HH2 as shown in Fig. 11.2(e) based on the principle of multiresolution analysis. Accordingly the image is decomposed into any number of levels. The same computation can continue to further decompose LL2 into higher levels.

Figure 8.2 Extension of DWT in two - dimensional signals



9. Methodology

The proposed method can be described as follows:

Step 1: Assuming a color image of size5 , 6, the 2D-DWT is performed and approximate coefficient matrix of size 7 8 is taken for the next step.

Step 2: Overlapping blocks of size 9 9 are created with one pixel shifting and the number of overlapping blocks for each layer is given b

:; = (? @ + ) (B @ + ) 9.1

Step 3: For every layer, each overlapping block is transformed into a row matrix (RM) with size :; @&.

Step 4: Rows representing the non-overlapping blocks are extracted from the matrix RM. For a block size of9 9, the number of non-overlapping blocks in each layer is given by

:C;C; = D? @E F (B @E ) 9.29.29.29.2

The elements of each non-overlapping row of RM matrix are sorted lexicographically. The correlation coefficient (Cij) among non-overlapping rows is calculated considering a pair of rows at a time resulting in Cij, where i = 1: Nnonover and j = (i+1) to Nnonover. This operation is carried out for three layers separately. Step 5: A threshold value (CT) of correlation coefficient is chosen. A pair of rows with all three

(for three layers) values of Cij greater than CT is selected as candidate block. Step 6: Distance is computed considering each candidate row and all other rows by using the

following formula:

+(H, I) = ( @&E )|HK IK| HKE

@&KL 9.3

Where P and Q represents two rows. Similar to Cij, three different distances will be obtained for three layers of the image. A threshold value (CD) of the distance between two rows is chosen. A pair of rows with average value of three distances less than CD is selected as forged block. Location of these forged blocks are marked in the original image by setting pixel values to zero.

Copy & Move Image Forgery Detection using

Electronics & Instrumentation Engineering

10. Result

The copy/move image forgery detection peforged test image. Forged images are prepared by copying some portion of the image and make it forged using copy /move technique in a standard format (JEPG). For performance evaluation, most widely used parameters, hit rate, miss rate, and false detection rate (FDR) are used, which can be defined as

BK(MN(= = OPNQ=

RK''MN(= = OPNQ

STM = OPNQ=+=(=U(=+

Here all the rates will be calculated based on pixel values.

Forged image is shown in fig. 10detect the copy move portion of the image.


Engineering Department

The copy/move image forgery detection performance of the proposed image is tested on any forged test image. Forged images are prepared by copying some portion of the image and make it forged using copy /move technique in a standard format (JEPG). For performance evaluation,

meters, hit rate, miss rate, and false detection rate (FDR) are used, which

OPNQ=+=(=U(=+N'V;>Q=+@=KCQV;>Q=+S;>Q=+OPNQ= WW

OPNQ=+=(=U(=+N'C;(V;>Q=+@=KCQV;>Q=+S;>Q=+OPNQ= WW

+=(=U(=+N'V;>Q=+@=KCQX>KQKCNYS;>Q=+OPNQ= WW

calculated based on pixel values.

10.1. The proposed algorithm is applied on the forged image to portion of the image.

Fig. 10.1 Forged Test Image

DWT based Hybrid Algorithm

Page 20

rformance of the proposed image is tested on any forged test image. Forged images are prepared by copying some portion of the image and make it forged using copy /move technique in a standard format (JEPG). For performance evaluation,

meters, hit rate, miss rate, and false detection rate (FDR) are used, which

10.1

10.2

10.3

algorithm is applied on the forged image to



Our proposed algorithm is applied on the test image and the various parameters will be calculated related to the Copy Move forgery detection.

First a forged image is taken and separates the three color layer of the image i.e. Red, Green, and Blue. Two dimensional Discrete Wavelet Transform is applied individually on all the three layer of forged image using Haar Wavelet. The output of red color of the first step is described in fig. 10.2. The output of green color of the first step is described in fig. 10.3. The output of blue color of the first step is described in fig. 10.4.

Fig. 10.2 DWT output of Red Layer of forged image.



Fig. 10.2 DWT output of Green Layer of forged image.



Fig. 10.2 DWT output of Blue Layer of forged image.

The above results are not complete result. I am still working on it to get the desired output and to make this algorithm robust and practical to use. These are the output of the three layer of forged image. Now the next step is to select a reference block then this reference block will be compared with the each and every overlapping block of the forged image to find the copied portion of the image. This can be finding with help of correlation coefficients matching process.



11. Conclusion

This report presents a scheme for copy-move forgery detection in digital images based on 2D-DWT based hybrid algorithm with the help of block matching procedure. The proposed reference block selection algorithm helps in avoiding the huge computational burden involved in block matching operation among all blocks. Moreover, in DWT coefficient matching, instead of using both approximate and detail coefficients, only the former type is employed. In the second stage, as the reference blocks are matched with all overlapping blocks, high level of detection accuracy is achieved both in case of single and multiple copy-move forgeries.



12. Reference [1] S. A. Fattah1*, M. M. I. Ullah1, M. Ahmed1, I. Ahmmed2, and C. Shahnaz1 A Scheme

for Copy-Move Forgery Detection in Digital Images Based on 2D-DWT in proc. 978-1-4799-4132-2/14/$31.00 2014 IEEE.

[2] M.Sridevi, C.Mala and S.Sandeep Copy Move Image Forgery Detection In A Parallel Environment in proc Natarajan Meghanathan, et al. (Eds): SIPM, FCST, ITCA, WSE, ACSIT, CS & IT 06, pp. 1929, 2012.

[3] Nathalie Diane Wandji1, Sun Xingming2, Moise Fah Kue3 Detection of copy-move forgery in digital images based on DCT supported by NSFC (61232016, 61173141, 61173142, 61173136, 61103215, 61070196, 61070195, and 61073191), National Basic Research Program 973 (2011CB311808), 2011GK2009, GYHY201206033, 201301030, 2013DFG12860 and PAPD.

[4] Li Jing, and Chao Shao Image Copy-Move Forgery Detecting Based on Local Invariant Feature Academy Publisher Journal of Multimedia, Vol. 7, No. 1, February 2012.

[5] M. Ali Qureshi, M.Deriche A Review on Copy Move Image Forgery Detection Techniques 978-1-4799-3866-7/14/$31.00 2014 IEEE.

[6] Ghulam Muhammad1, M. Solaiman Dewan2, M. Moniruzzaman3, Muhammad Hussain1, and M. Nurul Huda4 Image Forgery Detection Using Gabor Filters And Dct International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) 2014.

[7] G. Muhammad, M. Hussain, K. Khawaji, and G. Bebis, Blind copy move image forgery detection using dyadic undecimated wavelet transform, in proc. Int. Conf. Digital Signal Processing (DSP), July 2011.

[8] H. Farid, Digital Doctoring: how to tell the real from the fake, Significance, vol. 3, no. 4, pp. 162-166, December 2006.

[9] H. Farid, A survey of image forgery detection, IEEE Signal Processing Magazine, vol. 26, no. 2, pp. 1625, March 2009.

[10] B. Mahdian and S. Saic, Using Noise Inconsistencies for Blind Image Forensics, Image and Vision Computing, vol. 27, no. 10, pp. 1497-1503, September 2009.



[11] J. Lukas, J. Fridrich, and M. Goljan, Detecting digital image forgeries using sensor pattern noise, in proc. Security, Steganography, Watermarking of Multimedia Contents VIII, part of EI SPIE 2006, January, 2006.

[12] V. Christlein, C. Rieses, J. Jordan, C. Rieses, E. Angelopoulou, An evaluation of popular copy-move forgery detection approaches, IEEE Trans. Information Forensics and Security, vol. 7, no. 6, pp. 1841-1854, December 2012.

[13] W. Lou, J. Huang and G. Qiu, Robust Detection of Region-Duplication Forgery in Digital Image, in proc. IEEE Int. Conf. Pattern Recognition, vol. 4, pp. 746-749, 2006.

[14] Y. Cao, T. Gao, L. Fan, Q. Yang, A Robust Detection Algorithm for Copy-Move Forgery in Digital Images, Forensic Science International, January 2012.

[15] E. Ardizzone,A . Bruno,a nd G. Mazzola," Copy-move forgery detection via texture description," in Proceedings of the 2nd ACM workshop o Multimedia in forensics, security and intelligence. NY, USA: ACM, 2010, pp. 59-64.

[16] A. J. Menezes, S. A. Vanstone, and P. C. V. Oorschot, Handbook of Applied Cryptography, 1st ed. Boca Raton, Fla, USA: CRC Press, 1996.

[17] H. Farid, "Image forgery detection," IEEE Signal Processing Magazine, vol. 26, no. 2, pp. 16-25, Mar 2009.

[18] S.-J. Ryu, M.-J. Lee, and H.-K. Lee, "Detection of copy-rotate-move forgery using zernike moments," in Information Hiding. Berlin, Heidelberg: Springer, 2010, pp. 51-65.

[19] S. Bayram, T. Sencar, N Memon An Efficient and Robust Method for Detecting Copy-move Forgery, ICASSP 2009, pp. 1053-1056.

[20] S. Khan, A. Kulkarni , Reduced Time Complexity for Detection of Copy-Move Forgery Using Discrete Wavelet Transform , in International Journal of Computer Applications Volume 6. No.7 September 2010.

[21] M. Zimba, S. Xingming , DWT-PCA (EVD) Based Copy-move Image Forgery Detection, in International Journal of Digital Content Technology and its Applications , January 2011, PP. 251-258.

[22] B. Mahdian and S. Saic, "Detection of copy-move forgery using a method based on blur moment invariants," Forensic Science International, vol. 171, no. 2, pp. 180189, 2007.



Work Allotted I have joined Shri G. S. Institute of Technology & Science, Indore on 16/06/2014 as a research fellow in electronics & Instrumentation engineering department. During my probation period I have assigned many departmental works like lecture, laboratory. The load of my teaching was one subject and two labs were assigned in a semester. I have also involved in NBA work. I am working on Copy/Move Image forgery detection using DWT Algorithm. I am still working on it to get the robust and hybrid copy/move image forgery detection algorithm.

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