Copy-Move Detection

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Counter-forensics of SIFT-based copy-move detection by means of keypoint classification

Counter-forensics of SIFT-based copy-move detection by means of keypoint classification

The research community has recently started to approach copy-move detection from the perspective of the attacker, whose goal is to hide the features caus- ing similar blocks or keypoints to match. Chrislein et al. [11] studied the robustness of several detectors against common image processing and observed that block-based methods are not robust against geometric manipulations (e.g., resampling, cropping), lossy compression, and noise addition. Nguyen et al. [12] successfully impaired three well-known block-based detectors by combining some of these manipulations into a simple yet effective counter- forensic scheme (see [12]). The same results, however, cannot be replicated with SIFT-based detectors inheriting the intrinsic robustness against geometric transforma- tions from Lowe’s algorithm. To devise more sophisticated schemes, it is necessary to understand the security of SIFT algorithm. The first study in this direction is the one by Hsu et al. [13], in which, initially, the impact of sim- ple attacks is analyzed, and then a method to strengthen SIFT keypoints is proposed. Following this work, Do et al. [14-16] focused on a SIFT-based content-based image retrieval (CBIR) [17] scenario and devised a number of interesting attacks.
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COPY MOVE FORGERY DETECTION AND ESTIMATION OF DIGITAL IMAGES: A SURVEY

COPY MOVE FORGERY DETECTION AND ESTIMATION OF DIGITAL IMAGES: A SURVEY

In 2007 Li et al [7] present a method for forgery detection in highly compressed and boundary processed images. Singular value decomposition is used for getting reduced dimension image and then wavelet to find forgery. Duplicated regions are sorted lexicographically and neighborhood detection for all blocks. In 2009 Zhang et al proposed SVD Based method for copy move detection. It was found that the SVD algorithm along with counting bloom filters result into improvement in time complexity [8]. In 2010 Christlein et al presented is dimensionality constant rotation invariant selection method called same affine transformation selection. It detects rotated and scaled region [9]. In 2010 Xu Bo et al proposed copy move forgery based on speed of robust descriptor [10]. In the same year Weihai et al proposed an algorithm based on Fourier Melina transform. In this method the features are extracted along the radius to improve time complexity. Link processes is introduced in counting bloom filter [11]. This algorithm shows that it is robust for large rotation of copy move region. In 2011 Bravo et al presented [12] a method which detects duplicated region, even if the duplicated region undergoes reflection, rotation and scaling. In this paper overlapping block pixels are mapped to log polar coordinates and then summed along the angle axis, to produce one dimensional descriptor invariant to reflection and rotation. For scaled region detection the reduced dimension representation of each block has great impact on computational cost. Here very few images are tested. False alarm can be minimized by using more number of features. In 2011 copy, move detection by using different type of moment was initiated.
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Copy and Move Detection in Audio Recordings using Dynamic Time Warping Algorithm

Copy and Move Detection in Audio Recordings using Dynamic Time Warping Algorithm

Audio recordings are used very abundantly as a digital evidences and proofs in courts[1]. Technology has also been increasing in a rapid way that more powerful software’s are developing in such a way that any normal human can tamper or modify the audio recording just by copying and pasting the audio segments in the positions where the user wants it. This is made very easy with the help of a professional software tools. For example “I had been to police station” can be changed very easily to “I had not been to police station” just by inserting not in between “been” and “had”. Just by changing the semantics from the same audio file it is possible to modify the whole sentence by using above example. For just to experiment it is fine but, in real life forensics copy move detection of audio recordings plays important role and is very popular. It is not so easy to detect the forged segment in the audio process without proper audio detection tool. Listening to the whole audio recordings over and over until we observe a slight change may take longer hours.
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Angular Radial Transform based Copy Move Forgery Detection

Angular Radial Transform based Copy Move Forgery Detection

Abstract— Digital images are most common and the convenient way of storing and transmitting the visual information. Copy move forgery is most common form local processing among other techniques. This paper proposes a dense field copy move detection technique based on Angular Radial Transform of small image blocks. We exploit rotation invariance properties to reliably unveil duplicated regions after arbitrary rotations. Experiments indicate high robustness against rotation JPEG compression, blurring, smooth and textured images.
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Copy Move Forgeries Detection and Localization Using Two Levels of  Keypoints Extraction

Copy Move Forgeries Detection and Localization Using Two Levels of Keypoints Extraction

The SURF algorithm had been proposed and deployed by Bay et al . [10] in 2006 to enhance the efficiency of SIFT. SURF is fast and robust feature detector. SURF speed is increased due to the integral image. SURF is invariant to scaling and rotation transformations. SURF detector algorithm is not proper for disco- vering the regions’ repetition in case of extremely compressed JPEG images and smooth copied regions. It is proper for non-flat regions. SURF algorithm is one of the major commonly used feature extraction algorithms because it can provide stable visual points called interest points, which are scale invariant and robust un- der a broad area of view and illustration changes. SURF provides comparable keypoints performance extraction and increased operating speed. Thus; SURF has been pulling a lot of concern in these days. They explained that SURF can minim- ize the false match specifically for the highly resolution images, whilst robust to specific transformation and post processing processes. However; SURF cannot detect a tiny copied area in the image. It was later shown that the SURF-based tech- nique reduced the accuracy although it improves the processing time in copy-move detection.
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Copy Move Image Forgery Detection Using SIFT

Copy Move Image Forgery Detection Using SIFT

This method requires original image area and the pasted area, by splitting the image into overlapping blocks and then a feature extraction process is applied through which the image blocks represented by a low dimensional representation. In the literature different block based representation has been proposed such as DCT (Discrete cosine transform), PCA (Principal component analysis) and DWT (Discrete wavelet transform) for both process of image splicing and copy move detection. Recently, the study proposed an approach of duplication detection which can adopt two robust features based on KPCA (Kernel Principal Fig.1: basic block diagram for
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COPY-MOVE FORGERY DETECTION USING ORB AND SIFT DETECTOR

COPY-MOVE FORGERY DETECTION USING ORB AND SIFT DETECTOR

IJEDR1604120 International Journal of Engineering Development and Research (www.ijedr.org) 806 copy-move forgery by copying and pasting a part in the same image with the intent of hiding unwanted regions. Copy- Move forgery detection techniques are mainly classified as: Block based copy-move detection techniques and Key-point based copy-move detection techniques [11].

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Passive Copy-Move Image Forgery Detection Method Using MSER and SVD

Passive Copy-Move Image Forgery Detection Method Using MSER and SVD

Digital Forensic is a branch of forensic science which is related to computer crime. It deals with the investigation and recovery of material found in digital devices. Digital images and videos are the most important part of digital forensics as they are the prime evidences in law issues or in mass media. So the loyalty of the digital image is important. Due to the rapid increase and easy access of photo editing software tools, it has become easy to duplicate and modify digital image. Copy-move forgery (CMF) is the widely recognized image forgery technique in digital images. In CMF, certain part of the image has been copied and subsequently pasted to another location within the original image to make duplication or cover something. A considerable number of researches already done on the copy-move forgery detection (CMFD) but there’s a problem occurs in feature extraction and matching phase. So in the proposed work, we design copy-move forgery detection system using the MSER feature extraction technique and correlation based matching on the feature vector. MSER is the application for the region presented in the image so we can find out the forged region easily. Experimental analysis shows that the proposed work achieves better accuracy on the basis of precision and recall rate compared to similar works.
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Parallel Copy Move Image Forgery Detection Using Lexicographic Sorting Algorithm

Parallel Copy Move Image Forgery Detection Using Lexicographic Sorting Algorithm

The use of digital camera has increased now a days, due to available advanced software, digital images which are usually took by digital camera has been done forgery by using these advanced software’s, it’s very difficult for individual to judge whether it is original image or forgery image and we have to able to detect this, in order to produce in court of law as proof of crime, but task of finding marks of altering in digital picture is thought-provoking work ,a copy move picture is ended by hiding the picture behind an entity or by totaling some minutiae general forgery applies several tasks, such as jpeg compression, and addition of noise to unique image formerly pasting, In the existing methods the feature extraction and the lexicographic sorting are done in sequential environment which is time consuming, but in this paper the proposed algorithm shows that the feature extraction and lexicographic sorting are done in the parallel environment which decreases execution time and this paper shows operational procedure of copy move image counterfeit constructed on likeness and relationship between original image fragments and glued one inside similar picture.
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Study and Analysis of Copy Move Forgery Detection in Digital Image using MATLAB

Study and Analysis of Copy Move Forgery Detection in Digital Image using MATLAB

Abstract- with the rapid development of ubiquitous availability of imaging tools and software, it is not difficult to tamper or forge the digital image. In today's digital age, it is feasible to add or remove important features from an image without any clear traces of alteration. So there is an imperative issue to identify the authenticity of digital images in various fields such as forensics, criminal investigation, surveillance system, intelligent system, medical imaging and Journalism. Digital Image Forensic is rising and swiftly growing field of image processing area to find the authenticity of digital image. Copy-Move attack is very common type of tampering technique, where a part of an image is copied and pasted elsewhere in the same image to conceal a special object in the original image. Many block based methods have been suggested to detect duplicated region (Copy-Move forgery).One of the major challenge of these block based is the time complexity. As the image size increases the execution time of such algorithm is also increases. In the proposed method, PCA is applied to the suspected image to reduce its dimensionality. Thus, Principal Component Analysis is for digital image compression. I have devised and implemented a side matching approach to reduce the time complexity. This proposed algorithm improves the time complexity in detection of Copy-Move Forgery.
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A Copy Move Technique Using Block wise Detection for an Image Forgery

A Copy Move Technique Using Block wise Detection for an Image Forgery

In this paper, we have introduced a method to find out the forged part of an image using block-wise division of an image. These blocks are of 64 pixels each. These blocks have been gone through Fast Fourier Transform. we discover the matter of recognizing the copy move falsification and hard identification policy. Our work will be based on an “Enhanced detection algorithm”. The system might effectively discriminate the created part actually when the duplicated zone is customized to union it with the base and when the fashioned image is spared in a lossy organization, for example, JPEG(joint photographic expert group). The execution of the proposed strategy is showed on a few produced images. This research paper gives the better results when we compared our results with previous results.
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Improved Copy-Move Forgery Detection Using Interest Point Detectors

Improved Copy-Move Forgery Detection Using Interest Point Detectors

Abstract: The main problem in the real world is to determine whether an image is forged or not. Any alteration in an original image in bad faith is called image forgery. CopyMove (region duplication) is a common attack in which at least one part of an image is copied and pasted onto another area of the same image to add or remove an object. Detection methods use either block-based methods, or point-based methods. Here we propose a novel copy-move forgery detection scheme that can accurately localize the tampered regions. A new interest point detector is proposed in which the detected key points adaptively cover the entire image, even low contrast regions, based on the uniqueness metric. An adaptive matching is performed to find the similar key points. Moreover, a new filtering algorithm is utilized, which can effectively remove the falsely matched regions. Then the whole procedure is iterated regarding the prior information.
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An Evaluation of Digital Image Forgery Detection  Based Copy Move Image Forgery Detection Techniques

An Evaluation of Digital Image Forgery Detection Based Copy Move Image Forgery Detection Techniques

detection. With DWT, the low frequency information or image is obtained. With SIFT robustness is introduced, here it detect forgery of the image even it is copied, rotate scale and then pasted. In [3], survey done on various image forgery detection techniques and finally conclude the comparative study with some parameters. Also tools are mentioned to detect the forged images that travel over the network or by natural way for daily forensics, image processing, and security. Salam A.Thajeel [4], discussed digital image forensics and its types, challenges and research problems and detail analysis of the existing approaches for detect image tampering. Author also discussed block based method and key point-based method and popular techniques of two methods. Moreover, most of the methods may not address the problems. Therefore, there is a need to develop techniques that is efficient to deal with these challenges. The Speeded Up Robust Features (SURF) [6] were applied to extract features instead of SIFT. However, although these methods can locate the matched key-points, most of them cannot locate the forgery regions very well; therefore, they cannot achieve satisfactory detection results and, at the same time, a sustained high recall rate [5]. A novel copymove forgery detection scheme using
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Title: A Review on Copy-Move Forgery Detection Techniques Based on DCT and DWT

Title: A Review on Copy-Move Forgery Detection Techniques Based on DCT and DWT

Image tampering is defined as a kind of forgery where some part of the image is added or removed in order to manipulate the information it conveys. Technically, the forgerer changes the pixel values and location depending on the motive, along with some transformations such as rotation, scaling etc. Image tampering comprises three types of forgery viz. cloning(copy-move),splicing and retouching. Cloning is a situation where some part of the image is copied or cloned and pasted on to another specific area of the same image. The sole motive here is to hide some important portion of the image. The situation becomes more cumbersome when some kinds of transformations are applied before pasting the region of interest and hence, these are harder to detect as properties of the copied and moved region remains the same. Second type of tampering includes image splicing which is a process that involves collecting specific regions from different images and assembling them onto a single image. Lastly, retouching involves enhancement of the image by adjusting colors, contrast, noise, sharpness etc.
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Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation

Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation

for an image. The method mainly used the mean inten- sities of a circle with different radii around the center of the block to represent the features of the block. Ryu et al. [3, 4] used Zernike moments as block features. The method can identify the forged region by copy-rotate- move forgery. Huang et al. [5] proposed a discrete cosine transform (DCT)-based forgery detection method. The image is first divided into overlapping blocks and the DCT is applied, thus the DCT coefficients for each block are quantized by fixed stepsize q and then rounded to the nearest integer. A row vector as block feature can then be obtained by using a zigzag scan. The duplicated image blocks are compared in the matching step. This method can detect JPEG compression, but the DCT-based feature vector cannot resist geometrical tampering.
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Graphics processing unit based parallel copy move image forgery detection scheme

Graphics processing unit based parallel copy move image forgery detection scheme

Before the issues that exist in block based approaches to copy move image forgery detection can be explored, the processes within a block based detection scheme must first be identified and explained. The common flow of block based copy move forgery detection can be seen in Figure 1.1. The input for the detection scheme is an image suspected to contain copy move forgery. Firstly, the image is segmented into overlapping blocks to separate the different image regions. The image region within each block is then goes through a feature extraction process which transforms raw pixel information into a set of image features. The resulting set of image features is then subjected to a similarity analysis process which identifies pairs of highly similar or identical image features. The final output of the detection scheme is a set of blocks suspected to be duplicates of one another.
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Comparative Analysis of Wavelet Transform Functions in Image Forgery Detection

Comparative Analysis of Wavelet Transform Functions in Image Forgery Detection

ABSTRACT: Image forgery detection system is used to identify the authenticity of an image. With the availability of digital software and high resolution hardware, digital images can be easily tampered and manipulated. This is because now a days digital images can be manipulated in such a way that forgery cannot be detected visibly. This vogue directs major compulsion and decline the reliability of digital images. This paper presents comparative results of Wavelet Coefficients for copy move image forgery detection.

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Review on local binary patterns variants as texture descriptors for copy-move forgery detection

Review on local binary patterns variants as texture descriptors for copy-move forgery detection

Zheng et al. [18] developed rotation invariance method that used texture features based on Local Binary Pattern (LBP) [19]-[22], where the features are directly extracted from each overlapping block. The proposed method has low computational time even though it does not convert colour images to grayscale. Additionally, it is not only invariant to the rotation but also robust to noise and blurring attacks. Another rotation invariance method has been proposed by [22] where they used LBP operator to describe the image texture from grayscale images. However, these images are contaminated with noise, lossy JPEG compression, and several other post-processing attacks that can cause high false positives. Thus, a Gaussian low-pass filter is used in pre-processing to improve image quality by removing noise contained therein where filtering by more than twice can increase the detection performances [23]. The properties of LBP is capable of reducing the computational complexity problem [24]. The matching process is done by calculating the Euclidean distances for each block of the image. Although this method is invariant to rotation and flipping, it cannot detect forgeries that involve rotation at different angles.
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Improve the Copy Move Forged Detection by Feature Selection and Matching by Ant Colony Optimization

Improve the Copy Move Forged Detection by Feature Selection and Matching by Ant Colony Optimization

Abstract: In our society digital images are a powerful and widely used communication medium. They have an important impact on communication and IT industry. The proposed versatile over division calculation sections the host picture into no overlapping and sporadic blocks adaptively. Then, the element focuses are removed from each block as block elements, and the block components are coordinated with each other to find the named highlight focuses; this technique can around show the presumed forgery districts. In past few years, research goes to detecting and classified for copy move forgery images for forensic requirement. So detection is very important challenges for testing in forensic science. In this paper detection and classification by point base and block base features SIFT and SURF Respectively but use ant colony optimization in matching and feature selection phases ,in case of SIFT features and proposed SIFT with ACO features which also use in classification with support vector machine with Gaussian and polynomial kernel.
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Blur-Invariant Copy-Move Forgery Detection Technique with Improved Detection Accuracy utilising SWT-SVD

Blur-Invariant Copy-Move Forgery Detection Technique with Improved Detection Accuracy utilising SWT-SVD

on, conventional copy-move forgery detection algorithms tend to produce huge FPR. This is due to the fact that in such cases, large parts of the image are naturally similar, and hence lead to incorrect detection results. To solve this problem, we adopt 8- connected neighbourhood checking [15]. Here, all the blocks that are detected to be duplicates are marked, and considered. For each marked block, its 8- connected neighbours, i.e. up, down, left, right and the four diagonals (as shown in Fig. 3), are checked. The number of neighbours that were also detected as duplicates by above subsection are counted. If this count is > x (some empirical value ≥ 1 and ≤ 7, say 4), then the original block is kept marked. Else, if the count is ≤ x, the original block is considered to be a false positive and hence unmarked.
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