Literatura académica sobre el tema "COPY-MOVE FORGERY"
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Artículos de revistas sobre el tema "COPY-MOVE FORGERY"
Alam, Md Iftekhar Hossian Md Tasnim y Jyotirmoy Ghose. "Image Forgery Detection Using Copy-Move Technique". International Journal of Research Publication and Reviews 4, n.º 3 (marzo de 2023): 1103–7. http://dx.doi.org/10.55248/gengpi.2023.32077.
Texto completoNaincy y Ashok Kumar Bathla. "Comparative Study and Survey on Copy Move Image Forgery Detection Approaches". Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, n.º 6 (30 de junio de 2015): 33–38. http://dx.doi.org/10.53555/nncse.v2i6.445.
Texto completoNaincy y Ashok Kumar Bathla. "Comparative Study and Survey on Copy Move Image Forgery Detection Approaches". Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, n.º 9 (30 de septiembre de 2015): 01–06. http://dx.doi.org/10.53555/nncse.v2i9.441.
Texto completoKashyap, Abhishek, Megha Agarwal y Hariom Gupta. "Detection of copy-move image forgery using SVD and cuckoo search algorithm". International Journal of Engineering & Technology 7, n.º 2.13 (15 de abril de 2018): 79. http://dx.doi.org/10.14419/ijet.v7i2.13.11604.
Texto completoPham, Nam Thanh, Jong-Weon Lee y Chun-Su Park. "Structural Correlation Based Method for Image Forgery Classification and Localization". Applied Sciences 10, n.º 13 (28 de junio de 2020): 4458. http://dx.doi.org/10.3390/app10134458.
Texto completoMallick, Devjani, Mantasha Shaikh, Anuja Gulhane y Tabassum Maktum. "Copy Move and Splicing Image Forgery Detection using CNN". ITM Web of Conferences 44 (2022): 03052. http://dx.doi.org/10.1051/itmconf/20224403052.
Texto completoQazi, Tanzeela, Mushtaq Ali, Khizar Hayat y Baptiste Magnier. "Seamless Copy–Move Replication in Digital Images". Journal of Imaging 8, n.º 3 (10 de marzo de 2022): 69. http://dx.doi.org/10.3390/jimaging8030069.
Texto completoGupta, Anil. "A New Copy Move Forgery Detection Technique using Adaptive Over-segementation and Feature Point Matching". Bulletin of Electrical Engineering and Informatics 7, n.º 3 (1 de septiembre de 2018): 345–49. http://dx.doi.org/10.11591/eei.v7i3.754.
Texto completoPrakash, Choudhary Shyam y Sushila Maheshkar. "Copy-Move Forgery Detection Using DyWT". International Journal of Multimedia Data Engineering and Management 8, n.º 2 (abril de 2017): 1–9. http://dx.doi.org/10.4018/ijmdem.2017040101.
Texto completoFu, Guiwei, Yujin Zhang y Yongqi Wang. "Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering". Applied Sciences 13, n.º 13 (26 de junio de 2023): 7528. http://dx.doi.org/10.3390/app13137528.
Texto completoTesis sobre el tema "COPY-MOVE FORGERY"
Khayeat, Ali. "Copy-move forgery detection in digital images". Thesis, Cardiff University, 2017. http://orca.cf.ac.uk/107043/.
Texto completoBhatnagar, Kunal y 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.
Texto completoEn 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.
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.
Texto completoChen, Ling-Ying y 陳怜穎. "Pyramid Structure for Copy-Move Forgery Detection". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/27911697635315331597.
Texto completo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.
Yang, Ging-Chu y 楊青矗. "Copy-Move Forgery Detection in Digital Image". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/7jw5nc.
Texto completo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.
Wang, Han y 王瀚. "Detecting Copy-Move Forgery Regions through Multi-Block Features". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/04495684675004787236.
Texto completo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.
Hao-ChiangHsu y 許豪江. "Detection of Copy-Move Forgery Image Using Gabor Descriptor". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/01528765039792755705.
Texto completo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.
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.
Texto completoDEEPAK. "A SUPER-SIFT APPROACH FOR COPY-MOVE FORGERY DETECTION". Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15910.
Texto completoChou, Chih-Hung y 周志鴻. "Robustness of Copy-Move Forgery Detection Against JPEG Compression Artifacts". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/96109216455528448807.
Texto completo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%.
Libros sobre el tema "COPY-MOVE FORGERY"
Soni, Badal y Pradip K. Das. Image Copy-Move Forgery Detection. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9.
Texto completoJ, Indumathi. State-of-The-Art in Block based Copy Move Forgery Detection: ICCS 2014. Editado por Kokula Krishna Hari K. Bangkok, Thailand: Association of Scientists, Developers and Faculties, 2014.
Buscar texto completoDas, Pradip K. y Badal Soni. Image Copy-Move Forgery Detection: New Tools and Techniques. Springer Singapore Pte. Limited, 2022.
Buscar texto completoCapítulos de libros sobre el tema "COPY-MOVE FORGERY"
Soni, Badal y Pradip K. Das. "Oriented FAST Rotated BRIEF and Trie-Based Efficient Copy-Move Forgery Detection Algorithm". En Image Copy-Move Forgery Detection, 101–29. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_8.
Texto completoSoni, Badal y Pradip K. Das. "Summing Up". En Image Copy-Move Forgery Detection, 131–33. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_9.
Texto completoSoni, Badal y Pradip K. Das. "Key-Points Based Enhanced CMFD System Using DBSCAN Clustering Algorithm". En Image Copy-Move Forgery Detection, 69–83. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_6.
Texto completoSoni, Badal y Pradip K. Das. "Copy-Move Forgery Detection Using Local Binary Pattern Histogram Fourier Features". En Image Copy-Move Forgery Detection, 33–42. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_3.
Texto completoSoni, Badal y Pradip K. Das. "Blur Invariant Block-Based CMFD System Using FWHT Features". En Image Copy-Move Forgery Detection, 43–50. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_4.
Texto completoSoni, Badal y Pradip K. Das. "Introduction". En Image Copy-Move Forgery Detection, 1–10. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_1.
Texto completoSoni, Badal y Pradip K. Das. "Geometric Transformation Invariant Improved Block-Based Copy-Move Forgery Detection". En Image Copy-Move Forgery Detection, 51–67. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_5.
Texto completoSoni, Badal y Pradip K. Das. "Background Study and Analysis". En Image Copy-Move Forgery Detection, 11–31. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_2.
Texto completoSoni, Badal y Pradip K. Das. "Image Copy-Move Forgery Detection Using Deep Convolutional Neural Networks". En Image Copy-Move Forgery Detection, 85–99. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9041-9_7.
Texto completoKharanghar, Monika y Amit Doegar. "Copy-Move Forgery Detection Methods: A Critique". En 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.
Texto completoActas de conferencias sobre el tema "COPY-MOVE FORGERY"
Patankar, A. B., A. Sukhpal, S. Shetye y A. ShroffShroff. "Copy move forgery detection". En International Conference & Workshop on Electronics & Telecommunication Engineering (ICWET 2016). Institution of Engineering and Technology, 2016. http://dx.doi.org/10.1049/cp.2016.1145.
Texto completoJaafar, Roqaya Hamad, Zahraa H. Rasool y Abbas H. Hassin Alasadi. "New Copy-Move Forgery Detection Algorithm". En 2019 International Russian Automation Conference. IEEE, 2019. http://dx.doi.org/10.1109/rusautocon.2019.8867813.
Texto completoBhavya Bhanu M P y Arun Kumar M N. "Copy-move forgery detection using segmentation". En 2017 11th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2017. http://dx.doi.org/10.1109/isco.2017.7855986.
Texto completoKarsh, Ram Kumar, Anurag Das, G. Lavanya Swetha, Abhishek Medhi, Rabul Hussain Laskar, Utkarsh Arya y Rohit Kumar Agarwal. "Copy-move forgery detection using ASIFT". En 2016 1st India International Conference on Information Processing (IICIP). IEEE, 2016. http://dx.doi.org/10.1109/iicip.2016.7975329.
Texto completoKang, Li y Xiao-ping Cheng. "Copy-move forgery detection in digital image". En 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5648249.
Texto completoGurbuz, Emre, Guzin Ulutas y Mustafa Ulutas. "Rotation invariant copy move forgery detection method". En 2015 9th International Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2015. http://dx.doi.org/10.1109/eleco.2015.7394451.
Texto completoPandey, Ramesh Chand, Sanjay Kumar Singh y K. K. Shukla. "Passive copy-move forgery detection in videos". En 2014 International Conference on Computer and Communication Technology (ICCCT). IEEE, 2014. http://dx.doi.org/10.1109/iccct.2014.7001509.
Texto completoTurk, Salih, Ozkan Bingol y Guzin Ulutas. "Detection of copy-move forgery using DoGCode". En 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130356.
Texto completoNguyen, Hieu Cuong y Stefan Katzenbeisser. "Security of copy-move forgery detection techniques". En ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946869.
Texto completoUlutas, G., M. Ulutas y V. V. Nabiyev. "Copy move forgery detection based on LBP". En 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531569.
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