Academic literature on the topic 'COPY-MOVE FORGERY'

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Dissertations / Theses on the topic "COPY-MOVE FORGERY"

1

Khayeat, Ali. "Copy-move forgery detection in digital images." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/107043/.

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The ready availability of image-editing software makes it important to ensure the authenticity of images. This thesis concerns the detection and localization of cloning, or Copy-Move Forgery (CMF), which is the most common type of image tampering, in which part(s) of the image are copied and pasted back somewhere else in the same image. Post-processing can be used to produce more realistic doctored images and thus can increase the difficulty of detecting forgery. This thesis presents three novel methods for CMF detection, using feature extraction, surface fitting and segmentation. The Dense Scale Invariant Feature Transform (DSIFT) has been improved by using a different method to estimate the canonical orientation of each circular block. The Fitting Function Rotation Invariant Descriptor (FFRID) has been developed by using the least squares method to fit the parameters of a quadratic function on each block curvatures. In the segmentation approach, three different methods were tested: the SLIC superpixels, the Bag of Words Image and the Rolling Guidance filter with the multi-thresholding method. We also developed the Segment Gradient Orientation Histogram (SGOH) to describe the gradient of irregularly shaped blocks (segments). The experimental results illustrate that our proposed algorithms can detect forgery in images containing copy-move objects with different types of transformation (translation, rotation, scaling, distortion and combined transformation). Moreover, the proposed methods are robust to post-processing (i.e. blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise) and can detect multiple duplicated objects. In addition, we developed a new method to estimate the similarity threshold for each image by optimizing a cost function based probability distribution. This method can detect CMF better than using a fixed threshold for all the test images, because our proposed method reduces the false positive and the time required to estimate one threshold for different images in the dataset. Finally, we used the hysteresis to decrease the number of false matches and produce the best possible result.
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Bhatnagar, Kunal, and Gustav Ekner. "Copy-move Image Forgery Detection with Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302507.

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Copy-move manipulation is a forgery method used on images where a small part is copied to another part. This thesis analyses the detection of copy-move forgeries with the help of Convolutional Neural Networks (CNN). The model used is utilizing an existing custom CNN layer to identify features useful for detecting manipulations. The model is trained and validated on data with different grades of manipulation to determine which combinations give the highest accuracy. The grades are determined by the copy-move size, ranging between 10% and 60% of the image size. The results show that training on images with more minor modifications generally gives better results than training on images with more considerable modifications, regardless of whether validated on small or large modified images. Also, it can be concluded that the particular convolutional layer, in general, is suitable for copy-move detection.<br>En copy-move manipulation är en förfalskningsmetod för bilder som går ut på att kopiera en liten del av en bild till en annan del. Den här rapporten analyserar detekteringen av copy-move-förfalskningar med hjälp av Convolutional Neural Networks (CNN). Modellen som används utnyttjar ett redan existerande CNN-lager skapat för att identifiera egenskaper i bilden användbara för detektering av bildmanipulation. Modellen är både tränad och validerad på data med olika grader av manipulation för att bestämma vilka kombinationer som ger högst träffsäkerhet. Skalan bestäms av storleken på copy-move-operationerna, med ett spann mellan 10% och 60% av bilden. Resultatet visar att träning med bilder med små modifikationer i allmänhet ger bättre resultat än att träna på bilder med större modifikationer, oavsett om valideringen skett på bilder av låg eller hög manipuleringsgrad. Det kan även konstateras att det särskilda CNN-lagret är lämpligt för detektering av copy-move-operationer.
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Li, Yuan Man. "SIFT-based image copy-move forgery detection and its adversarial attacks." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3952093.

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Chen, Ling-Ying, and 陳怜穎. "Pyramid Structure for Copy-Move Forgery Detection." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/27911697635315331597.

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碩士<br>淡江大學<br>資訊工程學系碩士班<br>103<br>This paper solves the passive copy-move detection efficiently. A copy-move attack is defined as a region of an image being replaced by a copy of other region in the same image. The proposed scheme improves the performance on the assumption of the copy-move area being larger than a predefined block size. Test image is partitioned to non-overlapping segmented block according to previous predefined block size. Each comparison block, which is overlapped extracted from a segmented block, is compared with upper-left comparison block of all segmented block. Experimental results show that the computation time can be greatly reduced with the similar performance to other conventional schemes.
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Yang, Ging-Chu, and 楊青矗. "Copy-Move Forgery Detection in Digital Image." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/7jw5nc.

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碩士<br>佛光大學<br>資訊學系<br>97<br>Digital images are easy to be tempered and edited due to availability of image editing software. Generally, image tampering protection can be divided into two kinds: (1) active protection: The original digital image is embedded with a watermark which can be used to detect tampering. (2) passive protection: it does not need any digital watermark or signature but relies on the image processing technology to detect the forgery. Passive approaches have not yet been thoroughly researched. The most common ways to temper a digital image is copy-paste forgery which is used to conceal objects or produce a non-existing scene. To detect the copy-paste forgery, we divide the image into blocks as the basic feature for detection, and transfer every block to a feature vector with lower dimension for comparison. The number of blocks and dimension of characteristics are the major factor affecting the computation complexity. In this paper, we modify the previous methods by using less cumulative offsets for block matching. The experimental results show that our method can successfully detect the forgery part even when the forged image is saved in a lossy format such as JPEG. The performance of the proposed method is demonstrated on several forged images.
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Wang, Han, and 王瀚. "Detecting Copy-Move Forgery Regions through Multi-Block Features." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/04495684675004787236.

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碩士<br>淡江大學<br>資訊工程學系碩士班<br>104<br>This paper identifies copy-move forgery regions in an image through invariant features extracted from each block. First, an image is divided into overlapped blocks and 7 invariant moment features of the circle area under each block are calculated. Two features, mean and variance, are then acquired from the 7 moment features in each block. Each block is only compared to those blocks under the intersection of the same mean and variance feature sets. The copy-move forgery regions can be found by matching the detected blocks with the identical distance. Moreover, the adopted moment features are efficient on detecting rotational blocks. Experimental results show that the proposed scheme detects rotational duplicated regions well.
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Hao-ChiangHsu and 許豪江. "Detection of Copy-Move Forgery Image Using Gabor Descriptor." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/01528765039792755705.

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碩士<br>國立成功大學<br>工程科學系碩博士班<br>100<br>With the advance of science and technology, images are easily accessible to everybody. It is also easy to be changed about the content. Images are usually as criminal evidences and news report. How to make sure if the content is not changed becomes a very important issue. In this research, the Gabor filter is mainly applied to get the features of the image under inspected. It is easy to get the rotation and/or scaling versions of the Gabor filter. An image is divided into overlapped sub-blocks with different block size. Each sub-block is convoluted with a proper Gabor filter with different rotation angle and scaling factor to get the called Gabor descriptor of the sub-block. These Gabor descriptors are conversed as the key point and feature vector of the sub-block. For comparing two sub-blocks, their Gabor descriptors are applied to find if there is any similarity between them. The proposed method not only can locate the duplicated regions precisely, but also estimate the rotation angle and scale factor of the inspected image. Experimental result shows that the proposed method can achieve high detection rate. It is also provided a good estimated rotation angle and scaling factor.
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D'Amiano, Luca. "A new technique for video copy-move forgery detection." Tesi di dottorato, 2017. http://www.fedoa.unina.it/12254/1/DAmiano_Luca_XXX.pdf.

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This thesis describes an algorithm for detecting copy-move falsifications in digital video. The thesis is composed of 5 chapters. In the first chapter there is an introduction to forgery detection for digital images and videos. Chapters 2, 3 and 4 describe in detail the techniques used for the implementation of the detection algorithm. The experimental results are presented in the fifth and last chapter.
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9

DEEPAK. "A SUPER-SIFT APPROACH FOR COPY-MOVE FORGERY DETECTION." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15910.

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Today’s technological era is described by the outspread of digital images. They are the most ordinary formation of conveying information whether through newspapers, internet, books, magazine, scientific journals or social media. They are used as a powerful proof against various crimes, frauds and as an evidence in various situations. With the evolution of image processing in past few years and many other image editing software, capturing, creating or altering images according to our perspective has become very simple and available. There are several kinds of image tampering like copy-move forgery, image enhancement, image splicing, image morphing, image retouching whereas copy-move forgery is the most frequent and trendy manipulation of digital images. In copy move forgery here, a part of particular image is copied and then pasted into that same image with the motive of veiling some important object or displaying a fictitious scenario. Because the duplicate or in other terms the copied portion comes from the same image, All the image properties like texture, noise, resolution, brightness, contrast will be suited with the original portion of the image making it more difficult for the experts to distinguish and detect the alteration. There are mostly two kinds of forgery detection techniques one is block based method and the other is based on key points. In past few years feature based approach like SIFT gain attention of researchers in the field of image forgery detection. I proposed a SUPER-SIFT method for copy move forgery detection. This work improves the fundamental concept of SIFT algorithm which is Feature Extraction. We use SISR for improving the quality of image. The proposed work consist of three main tasks, firstly we preprocess the input image with SISR algorithm to get a high resolution image. Then on high resolution image we apply SIFT algorithm for keypoint detection. After that we apply a fast potential based hierarchical agglomerative clustering method on the output of previous step to filter out the false matches and to groups the key points that have the same affine transform. On the basis of number of key points in a particular cluster, it can be said that the image having forgery or not. The experimental outcome shows that the proposed approach for the detection of copy-move forgery is efficient and powerful even when the copied portion undergoes various transformations like rotation, shearing, scaling or other post processing like adding noise and blurring.
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10

Chou, Chih-Hung, and 周志鴻. "Robustness of Copy-Move Forgery Detection Against JPEG Compression Artifacts." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/96109216455528448807.

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碩士<br>大葉大學<br>電機工程學系<br>102<br>In recent years, the popularity of digital cameras, smart phones and tablet computers has made the acquisition of digital images become easier. In addition, modern photo-editing software package such as Photoshop and PhotoImpact makes it relatively simple to create digital image forgeries, on which people almost cannot perceive the difference between the original image and its tampered version. The most common approach used to create a digital image forgery is the so-called copy-move method, which copies a specific block of image and then pastes it into another region in the same image to achieve information hiding. Most forgery images are delivered over internet to achieve information hiding for confusing the publics. Usually, digital images are compressed to some extent to save bandwidth prior to delivery. Compression inevitably destroys the feature such as gray intensity of that image and makes copy-move forgery detection becomes difficult. Therefore, keeping stable detection rate under different compression ratios is the major purpose of this study. In this paper, three different feature extraction methods, namely the principal component analysis (PCA), singular value decomposition (SVD) and Fourier transform method (FFT), are used to capture the feature of “variance” of scanning blocks. The Euclidean distance is adopted to match the original and duplicated blocks. Finally, the offset of block coordinates are counted and output the matching points greater than the preset threshold. In this thesis, we chose five real world images to test the robustness of the proposed method. Experimental results show that 100% accuracy rate and 1% or less false detection rate can be achieved for uncompressed images. Moreover, the proposed method can achieve 99% accuracy rate with less than 7% false detection rate even if the compression factor is as low as 20%.
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