Дисертації з теми "COPY-MOVE"
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1
Khayeat, Ali. "Copy-move forgery detection in digital images." Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/107043/.
Повний текст джерелаАнотація:
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.
2
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.
Повний текст джерелаАнотація:
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.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.
3
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.
Повний текст джерела
4
Prokop, Jeremy W. "The SRY Gene and Reductionism in Molecular Biology: How to Move from the Benchtop to a Systems Approach." University of Akron / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1373365929.
Повний текст джерела
5
Chen, Ling-Ying, and 陳怜穎. "Pyramid Structure for Copy-Move Forgery Detection." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/27911697635315331597.
Повний текст джерелаАнотація:
碩士淡江大學
資訊工程學系碩士班
103
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.
6
Yang, Ging-Chu, and 楊青矗. "Copy-Move Forgery Detection in Digital Image." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/7jw5nc.
Повний текст джерелаАнотація:
碩士佛光大學
資訊學系
97
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.
7
Wang, Han, and 王瀚. "Detecting Copy-Move Forgery Regions through Multi-Block Features." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/04495684675004787236.
Повний текст джерелаАнотація:
碩士淡江大學
資訊工程學系碩士班
104
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.
8
Hao-ChiangHsu and 許豪江. "Detection of Copy-Move Forgery Image Using Gabor Descriptor." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/01528765039792755705.
Повний текст джерелаАнотація:
碩士國立成功大學
工程科學系碩博士班
100
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.
9
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.
Повний текст джерелаАнотація:
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.
10
DEEPAK. "A SUPER-SIFT APPROACH FOR COPY-MOVE FORGERY DETECTION." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15910.
Повний текст джерелаАнотація:
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.
11
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.
Повний текст джерелаАнотація:
碩士大葉大學
電機工程學系
102
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%.
12
Chen, Chih-Ching, and 陳志清. "An Exploration of Benford Law in Copy-Move Forgery Image Forensics." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/94618035081679793386.
Повний текст джерелаАнотація:
碩士國立雲林科技大學
資訊管理系碩士班
102
Due to the popularity of the digital camera, digital images are everywhere in our daily lives in this digital era. Digital images play important roles in many fields. However, digital data is easy to be modified. With the progress of the image processing tools, the forgery and authenticity of digital images have become an important issue. According to the previous studies, it was not easy to use statistics ways to detect the copy-move blocks on the image blocks. Benford Law was used in the audit field. It had the characteristic of auditing the accounting fraud. With this feature, we can detect the probability of digital image forgery. We propose a digital detecting system based on Benford Law, and we examine the image fakery with the detecting feature and edge detection. Based on our image fakery detecting system, the result shows that using different cameras and Euclidean distance can effectively detect the edge of copy-move.
13
Wu, Tsz-An, and 吳慈安. "An Integrated Technique for Splicing and Copy-move Forgery Image Detection." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/51062184224375026870.
Повний текст джерелаАнотація:
碩士國立東華大學
資訊工程學系
99
Digital images can be easily forged with various image processing tools; nowadays the issue of digital image forgery becomes more and more important. As JPEG has been popularly used in image compression standard, forgery detection of JPEG images plays an important role now. Forging on compressed images often involves recompression and tends to erase those forging traces existed in un-compressed images. We could, however, attempt to discover new traces caused by recompression and use these traces to detect the recompressed image’s forgery. The artifacts introduced by lossy JPEG compression can be regarded as an inherent feature for recompressed images. In this thesis, a novel forgery image detection for splicing and copy-move forgery image is proposed. We first use a forgery image detection approach by periodicity analysis with the double compression effect in spatial and DCT domain. Then, the feature extracted by SURF descriptors is applied to resisting the variation of rotation and/or scaling. Experimental results demonstrate that the proposed technique is performed well on the detection of forgery localization. Especially for the copy-move forgery images, the proposed technique is able to locate the forged regions and recognize the non-original regions.
14
Lu, Wei-Yu, and 盧威宇. "Detecting Copy-Move Forgery Regions through SIFT and Region Growing Strategies." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/7ne8y4.
Повний текст джерелаАнотація:
碩士淡江大學
資訊工程學系碩士班
105
Since digital cameras and smart phones have become an indispensable part of life, a lot of digital images are widely used. However, digital image has the property of easy to modify through image editing software, the ability to find real image is a great challenge. This paper presents an algorithm to detect copy-move image regions by SIFT keypoints and region growing technique. First, the SIFT keypoints denotes important image’s feature points. By using scale similarity comparison technique, the similar starting 99 pair blocks are acquired. The region growing technique is adopted to generate the copy-move regions. Experimental results show that the SIFT keypoints are useful to detect starting copy-move blocks and further image-growing technique can detect copy-move regions effectively.
15
Shandilya, Meenal. "Detection of Geometric Transformations in Copy-Move Forgery of Digital Images." Thesis, 2015. http://ethesis.nitrkl.ac.in/7590/1/2015_MT_Detection_Shandila.pdf.
Повний текст джерелаАнотація:
Digital Forensics is a branch of forensic science which is related to cyber crime. It basically involves the detection, recovery and investigation of material found in digital devices. Digital images and videos plays most important role in digital forensics. They are the prime evidences of any crime scene. So the fidelity of the image is important. Digital images can be easily manipulated and edited with the help of image processing tools. Copy-move Forgery is the most primitive form of cyber attack on digital images. In Copy-move forgery a part of image (region) itself is copied and pasted into another part of the same image. The intension behind this type of attack is to “add” or “disappear” some objects from the image. Hence to break the fidelity of the image and fool the viewer. Copy-move attack is more prevalent in images having uniform texture or patterns, for e.g. sand, grass, water etc. In this thesis exact block matching is used as a detection technique. This technique is based on block matching, for these the whole image is divided into number of block and then the matching process is applied. Sometimes the copied region is processed before pasted i.e. some geometric transformations is applied on the pasted region. The transformations like scaling, rotation etc. It is not possible for human eyes to detect such kind of forgeries. Whenever forgery is done in this manner the common techniques like block matching, exhaustive search, auto-correlation and robust match etc. are not able to detect the forgery having geometric transformations. So that for identification of forged region we need some technique which are based on local features and also invariant to transformations. In this thesis SIFT is used for forgery detection. SIFT stands for Scale Invariant Feature Transform, this gives local feature points which are invariant to scales. The key points helps to find the duplicated region with different matching algorithms
16
Chang, Yi-Cheng, and 張逸丞. "An Efficiency Enhanced Cluster Expanding Block Algorithm for Copy-Move Forgery Detection." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/06126375561288108204.
Повний текст джерелаАнотація:
碩士淡江大學
資訊工程學系碩士班
103
This paper presents an algorithm for identifying copy-move forgery area in an image using efficient enhanced cluster expanding blocks strategy. First, the image is divided into overlapping blocks and two features, mean and variance, are calculated from each block. These two features are sorted and grouped into two different kinds of clusters. Combining a cluster with its neighboring cluster forms a bucket. The proposed scheme then uses expanding block method to compare all blocks in each bucket. Finally, the duplicated regions are found by matching detected blocks horizontally and vertically. Experimental results show that the proposed approach detects duplicated regions well under JPEG compression and Gaussian blurring.
17
SAPRA, PRINCE. "STUDY AND IMPLEMENTATION OF COPY-MOVE FORGERY DETECTION METHODS IN IMAGE PROCESSING." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18180.
Повний текст джерелаАнотація:
This research work is based on copy-move forgery detection. The copy-move forgery detection
technique has various phases which include pre-processing, feature extraction and marking of
forgery part on the image. In the previous methodology PCA algorithm was used for the feature
reduction in the copy-move forgery detection. The parameters which are taken as input by the
PCA algorithm for the feature reduction were defined statically. In this research work, to
improve performance of the copy-move forgery detection model, PCA parameters needs to
define dynamically.
The GLCM algorithm is used for the feature extraction in this research work. The GLCM
algorithm extract 13 textural features and mean of all features will be given as input to the PCA
algorithm. The initial parameters of PCA algorithm will be defined dynamically for the copymove forgery detection. At last Euclidean distance is calculated between the block pixel to
define forgery portion in the image. The performance of the proposed algorithm is tested in terms
of precision, recall and F-measure. It is found that proposed algorithm performs better as
compared to existing method in terms of all three parameters.
18
Chou, Tsung-Hsuan, and 周仲軒. "Investigation on Copy-Move Detection by Multi-Block Features and Expanding Block Strategies." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3gpnpd.
Повний текст джерелаАнотація:
碩士淡江大學
資訊工程學系碩士在職專班
105
The thesis investigates features on detecting copy-move duplicated regions. The structure of copy-move detection is searching keypoints through the Scale Invariant Feature Transform(SIFT), matched blocks acquired from these keypoints by invariant moments, region growing by surrounding matched blocks. The analyses include the Scale Invariant Feature Transform(SIFT) for calculating keypoints, keypoints match, invariant moments comparisons, sizes of region growing blocks. This thesis examines various parameters and thresholds of the adopted structure. We also find that there are many factors to affect the detected results. Three conclusions are summarized. First, Hu’s invariant moments are better than Zernike invariant moments. Second, positions of duplicated regions can be acquired from keypoints through a robust neighboring search. Third, the optimal growing block size is then acquired. At last, a set of optimal parameters are found through the exhausted experimental results.
19
Xi-FuYang and 楊錫府. "Detecting Splicing and Copy-move Forgeries in Images Based on Convolutional Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5u5adf.
Повний текст джерелаАнотація:
碩士國立成功大學
電腦與通信工程研究所
107
With the Internet development and the availability of image editing tools, digital images can be easily manipulated and edited. Therefore, the credibility of digital images has faced severe challenges. In digital image forensic, the copy-move and splicing forgeries are popular forgery attacks. For copy-move forgery, a part of the image is copied and pasted elsewhere in the same image in order to cover possible important messages. However, the image splicing is to duplicate a region of another image to the original image so as to add the contents not belonging to the original image. In this thesis, a convolutional neural network (CNN) model is proposed to detect such tampering. First, the image is divided into fixed-size non-overlapping patches and the Radon transform is applied to each patch to compute the features. After the network is trained, the proposed model can classify the tampered and the authentic patches. By classifying each patch in the images, the duplicated regions can be detected. The experimental results demonstrate that the accuracy of proposed method is better than other methods.
20
TSAI, YI-LIN, and 蔡宜霖. "Image Copy-move Forgery Detection Using Color Features and Hierarchical Feature Point Matching." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/cq949m.
Повний текст джерелаАнотація:
碩士國立中正大學
資訊工程研究所
107
In recent years, with rapid development of multimedia applications, digital images are easily forged. Copy-move forgery is a common type of forged images. With the advancement of technology, many types of copy-move forgery detection approaches had been proposed. Most of these approaches can be roughly divided into two categories: block-based and feature point-based. In this study, an image copy-move forgery detection using color features and hierarchical feature point matching is proposed. First, in order to extract enough feature points, image enhancement is used in the HSI color space for pre-processing. Next, enlarging input image and determining contrast threshold to extract enough feature points. There will be some feature point matching problems in the previous step. Therefore, a hierarchical feature point matching was employed to deal with these problems. In order to reduce the false matching, an iterative localization scheme is employed to remove the isolated matching pairs. Then, the forgery regions are generated after finishing the iterations. Finally, morphological close operation is used to make the forgery regions more accurately. Based on the experimental results, the performance of the proposed approach is better than those of four comparison approaches.
21
KUMAR, AKSHAT. "DETECTING DUPLICATE REGIONS IN DIGITAL IMAGES USING IMPROVED LOCALIZATION METHOD." Thesis, 2014. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15630.
Повний текст джерелаАнотація:
Now days, the uses of digital images has increased and so has the forgery mechanisms. Copy-Move forgery is one of the types of image forgery. In the case of copy-move forgery one small region of image is replaced with another region within the same image. Many algorithms already developed for detecting copy-move regions but the major problem for most of the algorithms are localization of duplicate regions within same image after detecting copy-move part. Here we present an efficient approach for detecting duplicate regions in a digital image. In our method, we have used PCA (Principal Component Analysis) to the input image for obtaining compressed image. For obtaining feature vector of each block, we divide the image into number of overlapping blocks. These feature vectors are calculated with the help of principal component analysis. Here we represent eigenvector as feature vector. Lexicographic sorting is henceforth applied to locate the similar feature vector of each block. If two or more feature vectors have same value then this indicates that image forgery has been done. But we cannot guarantee of image forgery because in some cases, feature vectors may be same in an image. Hence, we calculate total number of connected blocks with in the same distance. If the numbers of blocks are greater than a threshold value then image has been forged by duplicate regions, but if numbers of blocks are less than a threshold value then image has not been forged. After that, localization method is used for getting number of pixels that have been copied and pasted. With the help of our proposed method, duplicate regions in an image can be detected more accurately and with less computational complexity as compared to other methods.
22
AMERINI, IRENE. "Image Forensics: sourceidentification and tamperingdetection." Doctoral thesis, 2010. http://hdl.handle.net/2158/520262.
Повний текст джерела