Готові списки джерел за темами / COPY-MOVE

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Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "COPY-MOVE"

Alam, Md Iftekhar Hossian Md Tasnim, and Jyotirmoy Ghose. "Image Forgery Detection Using Copy-Move Technique." International Journal of Research Publication and Reviews 4, no. 3 (March 2023): 1103–7. http://dx.doi.org/10.55248/gengpi.2023.32077.

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Kuznetsov, A. V., and V. V. Myasnikov. "COPY-MOVE IMAGE FORENSICS DETECTION." Computer Optics 37, no. 2 (January 1, 2013): 244–53. http://dx.doi.org/10.18287/0134-2452-2013-37-2-244-253.

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Wang, Yitian, and Sei-ichiro Kamata. "Copy Move Image Forgery Detection Based on Polar Fourier Representation." International Journal of Machine Learning and Computing 8, no. 2 (April 2018): 158–63. http://dx.doi.org/10.18178/ijmlc.2018.8.2.680.

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Imannisa Rahma, Firstyani, and Ema Utami. "Gaussian Pyramid Decomposition in Copy-Move Image Forgery Detection with SIFT and Zernike Moment Algorithms." Telematika 15, no. 1 (February 28, 2022): 1–13. http://dx.doi.org/10.35671/telematika.v15i1.1322.

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Анотація:

One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.

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Prakash, Choudhary Shyam, and Sushila Maheshkar. "Copy-Move Forgery Detection Using DyWT." International Journal of Multimedia Data Engineering and Management 8, no. 2 (April 2017): 1–9. http://dx.doi.org/10.4018/ijmdem.2017040101.

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In this paper, we proposed a passive method for copy-move region duplication detection using dyadic wavelet transform (DyWT). DyWT is better than discrete wavelet transform (DWT) for data analysis as it is shift invariant. Initially we decompose the input image into approximation (LL1) and detail (HH1) sub-bands. Then LL1 and HH1 sub-bands are divided into overlapping sub blocks and find the similarity between the blocks. In LL1 sub-band the copied and moved blocks have high similarity rate than the HH1 sub-band, this is just because, there is noise inconsistency in the moved blocks. Then we sort the LL1 sub-band blocks pair based on high similarity and in HH1 blocks are sorted based on high dissimilarity. Then we apply threshold to get the copied moved blocks. Here we also applied some post processing operations to check the robustness of our method and we get the satisfactory results to validate the copy move forgery detection.

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Gürbüz, Emre, Guzin Ulutas, and Mustafa Ulutas. "Source-destination discrimination on copy-move forgeries." Multimedia Tools and Applications 80, no. 8 (January 12, 2021): 12831–42. http://dx.doi.org/10.1007/s11042-020-10436-0.

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Kaur, Sharanjit, and Manpreet Kaur. "Novel Method for Copy-Move Forgery Detection." International Journal of Computer Applications 174, no. 18 (February 16, 2021): 10–14. http://dx.doi.org/10.5120/ijca2021921064.

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Rao, Dr Tekuru Chandra Sekhar. "Copy Move Forgery Detection Using Hybrid Algorithm." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (August 25, 2020): 5071–76. http://dx.doi.org/10.30534/ijatcse/2020/128942020.

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Kumar, T. Sudheer. "Copy-Move Forgery Detection Using Moment Invariants." International Journal for Research in Applied Science and Engineering Technology 6, no. 1 (January 31, 2018): 1545–50. http://dx.doi.org/10.22214/ijraset.2018.1236.

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Qazi, Tanzeela, Mushtaq Ali, Khizar Hayat, and Baptiste Magnier. "Seamless Copy–Move Replication in Digital Images." Journal of Imaging 8, no. 3 (March 10, 2022): 69. http://dx.doi.org/10.3390/jimaging8030069.

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Анотація:

The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy–move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy–move forgery.

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Дисертації з теми "COPY-MOVE"

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.
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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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.

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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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Анотація:

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.

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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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Анотація:

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.

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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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Анотація:

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.

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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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Анотація:

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.

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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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Анотація:

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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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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Книги з теми "COPY-MOVE"

Soni, Badal, and Pradip K. Das. Image Copy-Move Forgery Detection. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9.

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J, Indumathi. State-of-The-Art in Block based Copy Move Forgery Detection: ICCS 2014. Edited by Kokula Krishna Hari K. Bangkok, Thailand: Association of Scientists, Developers and Faculties, 2014.

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Philadelphia fire: A novel. New York: Holt, 1990.

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Philadelphia fire. New York: Houghton Mifflin Co., 2005.

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Wideman, John Edgar. Philadelphia fire: A novel. New York: Vintage Books, 1991.

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On The Move Copy Colour. Golden Books Publishing Company, Inc., 1992.

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Move to Strike Proof Copy. Piatkus Books, 2001.

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Shaped Copy Colouring - on the Move. Egmont Childrens Books, 1994.

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Copy Colour and Trace : On the Move. Egmont Childrens Books, 1991.

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Das, Pradip K., and Badal Soni. Image Copy-Move Forgery Detection: New Tools and Techniques. Springer Singapore Pte. Limited, 2022.

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Частини книг з теми "COPY-MOVE"

Soni, Badal, and Pradip K. Das. "Oriented FAST Rotated BRIEF and Trie-Based Efficient Copy-Move Forgery Detection Algorithm." In Image Copy-Move Forgery Detection, 101–29. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_8.

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Soni, Badal, and Pradip K. Das. "Summing Up." In Image Copy-Move Forgery Detection, 131–33. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_9.

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Soni, Badal, and Pradip K. Das. "Key-Points Based Enhanced CMFD System Using DBSCAN Clustering Algorithm." In Image Copy-Move Forgery Detection, 69–83. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_6.

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Soni, Badal, and Pradip K. Das. "Copy-Move Forgery Detection Using Local Binary Pattern Histogram Fourier Features." In Image Copy-Move Forgery Detection, 33–42. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_3.

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Soni, Badal, and Pradip K. Das. "Blur Invariant Block-Based CMFD System Using FWHT Features." In Image Copy-Move Forgery Detection, 43–50. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_4.

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Soni, Badal, and Pradip K. Das. "Introduction." In Image Copy-Move Forgery Detection, 1–10. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_1.

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Soni, Badal, and Pradip K. Das. "Geometric Transformation Invariant Improved Block-Based Copy-Move Forgery Detection." In Image Copy-Move Forgery Detection, 51–67. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_5.

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Soni, Badal, and Pradip K. Das. "Background Study and Analysis." In Image Copy-Move Forgery Detection, 11–31. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_2.

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Soni, Badal, and Pradip K. Das. "Image Copy-Move Forgery Detection Using Deep Convolutional Neural Networks." In Image Copy-Move Forgery Detection, 85–99. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_7.

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Kharanghar, Monika, and Amit Doegar. "Copy-Move Forgery Detection Methods: A Critique." In Advances in Information Communication Technology and Computing, 501–23. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5421-6_49.

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Тези доповідей конференцій з теми "COPY-MOVE"

Patankar, A. B., A. Sukhpal, S. Shetye, and A. ShroffShroff. "Copy move forgery detection." In International Conference & Workshop on Electronics & Telecommunication Engineering (ICWET 2016). Institution of Engineering and Technology, 2016. http://dx.doi.org/10.1049/cp.2016.1145.

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Jaafar, Roqaya Hamad, Zahraa H. Rasool, and Abbas H. Hassin Alasadi. "New Copy-Move Forgery Detection Algorithm." In 2019 International Russian Automation Conference. IEEE, 2019. http://dx.doi.org/10.1109/rusautocon.2019.8867813.

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Bhavya Bhanu M P and Arun Kumar M N. "Copy-move forgery detection using segmentation." In 2017 11th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2017. http://dx.doi.org/10.1109/isco.2017.7855986.

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Karsh, Ram Kumar, Anurag Das, G. Lavanya Swetha, Abhishek Medhi, Rabul Hussain Laskar, Utkarsh Arya, and Rohit Kumar Agarwal. "Copy-move forgery detection using ASIFT." In 2016 1st India International Conference on Information Processing (IICIP). IEEE, 2016. http://dx.doi.org/10.1109/iicip.2016.7975329.

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Kang, Li, and Xiao-ping Cheng. "Copy-move forgery detection in digital image." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5648249.

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Gurbuz, Emre, Guzin Ulutas, and Mustafa Ulutas. "Rotation invariant copy move forgery detection method." In 2015 9th International Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2015. http://dx.doi.org/10.1109/eleco.2015.7394451.

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Pandey, Ramesh Chand, Sanjay Kumar Singh, and K. K. Shukla. "Passive copy-move forgery detection in videos." In 2014 International Conference on Computer and Communication Technology (ICCCT). IEEE, 2014. http://dx.doi.org/10.1109/iccct.2014.7001509.

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Turk, Salih, Ozkan Bingol, and Guzin Ulutas. "Detection of copy-move forgery using DoGCode." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130356.

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Liu, Zihan, and Wei Lu. "Fast Copy-Move Detection of Digital Audio." In 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC). IEEE, 2017. http://dx.doi.org/10.1109/dsc.2017.11.

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Nguyen, Hieu Cuong, and Stefan Katzenbeisser. "Security of copy-move forgery detection techniques." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946869.

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Звіти організацій з теми "COPY-MOVE"

Tautges, Timothy J., and Rajeev Jain. Mesh Copy/Move/Merge Tool for Reactor Simulation Applications. Office of Scientific and Technical Information (OSTI), April 2014. http://dx.doi.org/10.2172/1171187.

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