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Journal articles on the topic 'Copy move detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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 (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 (2013): 244–53. http://dx.doi.org/10.18287/0134-2452-2013-37-2-244-253.

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3

Prasad Patnayakuni, Siva. "Copy Move Forgery Detection Using an Effective CNN Model." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 758–64. http://dx.doi.org/10.21275/sr22710130316.

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Kaur, Harpreet, Jyoti Saxena, and Sukhjinder Singh. "Key-Point Based Copy-Move Forgery Detection and Their Hybrid Methods: A Review." Journal of Advance Research in Electrical & Electronics Engineering (ISSN: 2208-2395) 2, no. 6 (2015): 06–12. http://dx.doi.org/10.53555/nneee.v2i6.189.

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Copy-move image forgery is one of the tampering techniques that need to be tackled with. Many copy-move forgery detection techniques such as exhaustive search, block and key-point matching based methods have been proposed for the detection of copy-move image forgery. Although key-point based methods were found better than block based methods in terms of computationalefficiency, space complexity and robustness against rotation and scaling. However, key-point based methods also possess a number of limitations. So, researchers have proposed many integrated methods to cope up with the limitations

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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 (2022): 1–13. http://dx.doi.org/10.35671/telematika.v15i1.1322.

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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

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Anjali, Diwan, Sharma Rajat, K. Roy Anil, and K. Mitra Suman. "Digital Image Tamperin Gdetection using sift Key-Point." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 1484–89. https://doi.org/10.35940/ijeat.B3761.029320.

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Copy-move imitation is a widespread and generally utilized operation to corrupt digital image. It is considered as the most effective research areas in the domain of blind digital image forensics area. Key point based totally identification techniques have been regarded to be very environment-friendly in exposing copy-move proof because of their steadiness against a number of attacks, as like large-scale geometric movements. Conversely, these techniques don’t have the capabilities to cope with the instances if copy-move forgeries only engage in minor or clean areas, the place the quantit

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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 (2018): 158–63. http://dx.doi.org/10.18178/ijmlc.2018.8.2.680.

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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 (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 s

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9

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

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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-ban

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10

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

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11

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 (2020): 5071–76. http://dx.doi.org/10.30534/ijatcse/2020/128942020.

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12

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

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13

Singh, Ruchita, Ashish Oberoi, and Nishi Goel. "Copy Move Forgery Detection on Digital Images." International Journal of Computer Applications 98, no. 9 (2014): 17–22. http://dx.doi.org/10.5120/17211-7437.

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14

Sun, Yu, Rongrong Ni, and Yao Zhao. "Nonoverlapping Blocks Based Copy-Move Forgery Detection." Security and Communication Networks 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/1301290.

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In order to solve the problem of high computational complexity in block-based methods for copy-move forgery detection, we divide image into texture part and smooth part to deal with them separately. Keypoints are extracted and matched in texture regions. Instead of using all the overlapping blocks, we use nonoverlapping blocks as candidates in smooth regions. Clustering blocks with similar color into a group can be regarded as a preprocessing operation. To avoid mismatching due to misalignment, we update candidate blocks by registration before projecting them into hash space. In this way, we c

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15

Dixit, Anuja, and R. K. Gupta. "Copy-Move Image Forgery Detection a Review." International Journal of Image, Graphics and Signal Processing 8, no. 6 (2016): 29–40. http://dx.doi.org/10.5815/ijigsp.2016.06.04.

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16

Ruikar, Priyanka, and Pravin Patil. "Copy Move Image Forgery Detection Using SIFT." Oriental journal of computer science and technology 9, no. 3 (2016): 235–45. http://dx.doi.org/10.13005/ojcst/09.03.09.

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In recent years the digital form of data allowing ease on to manipulation & storage due to progress in technology. But this progress in technology has lots of risks especially when it comes to the security of the digital data & files. Basically, image forgery means malfunctioning & playing with images or manipulating data fraudulently. In that case, some important data may get hidden in the original image. In particular, many organizations worry for digital forgery, because it is easier to create fake & fraudulent images without leaving any Tampering traces. A copy-move is a sp

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17

Cozzolino, Davide, Giovanni Poggi, and Luisa Verdoliva. "Efficient Dense-Field Copy–Move Forgery Detection." IEEE Transactions on Information Forensics and Security 10, no. 11 (2015): 2284–97. http://dx.doi.org/10.1109/tifs.2015.2455334.

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18

Pavlović, Aleksandra, Natasa Glišović, Ana Gavrovska, and Irini Reljin. "Copy-move forgery detection based on multifractals." Multimedia Tools and Applications 78, no. 15 (2019): 20655–78. http://dx.doi.org/10.1007/s11042-019-7277-1.

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19

Kuznetsov, A. V., and V. V. Myasnikov. "New scheme for fast copy-move detection." Journal of Physics: Conference Series 1096 (September 2018): 012030. http://dx.doi.org/10.1088/1742-6596/1096/1/012030.

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20

Mushtaq, Saba, and Ajaz Hussain Mir. "Image Copy Move Forgery Detection: A Review." International Journal of Future Generation Communication and Networking 11, no. 2 (2018): 11–22. http://dx.doi.org/10.14257/ijfgcn.2018.11.2.02.

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21

Rajput, G. G., Smruti Dilip Dabhole, and Prashantha. "Modified Keypoint-Based Copy Move Area Detection." Procedia Computer Science 235 (2024): 3389–96. http://dx.doi.org/10.1016/j.procs.2024.04.319.

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22

Abdalla, Younis, M. Iqbal, and Mohamed Shehata. "Convolutional Neural Network for Copy-Move Forgery Detection." Symmetry 11, no. 10 (2019): 1280. http://dx.doi.org/10.3390/sym11101280.

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Digital image forgery is a growing problem due to the increase in readily-available technology that makes the process relatively easy. In response, several approaches have been developed for detecting digital forgeries. This paper proposes a novel scheme based on neural networks and deep learning, focusing on the convolutional neural network (CNN) architecture approach to enhance a copy-move forgery detection. The proposed approach employs a CNN architecture that incorporates pre-processing layers to give satisfactory results. In addition, the possibility of using this model for various copy-m

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23

Lovepreet, Kaur*1 &. Sandeep Singh Dhaliwal2. "COPY MOVE FORGERY DETECTION IN DIGITAL IMAGES USING IMPROVED SIFT (I-SIFT) APPROACH." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 9, no. 3 (2020): 6–12. https://doi.org/10.5281/zenodo.3700407.

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With this across the board use of computerized pictures, notwithstanding the expanding number of instruments and programming of advanced pictures altering, it has ended up being definitely not hard to control and change the genuine information of the image. Existing system for forgery detection has many problems like maximum angle that existing system can detect is 40 degree rotation. Existing systems cannot detect forgery if duplicate content is compressed or enhanced. In the proposed system, we have developed a novel approach namedI-SIFT for copy move forgery detection that can detect the co

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24

Sekhar, Resmi, and R. S. Shaji. "A Methodological Review on Copy-Move Forgery Detection for Image Forensics." International Journal of Digital Crime and Forensics 6, no. 4 (2014): 34–49. http://dx.doi.org/10.4018/ijdcf.2014100103.

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Copy-Move forgery is the very prevalent form of image tampering. The powerful image processing tools available freely helps even the naive to tamper with images. A copy-move forgery is performed by copying a region in an image and pasting it in the same image most probably after applying some form of post-processing on the region like rotation, blurring, scaling, double JPEG compression etc. This makes it difficult to develop one common technique to detect copy-move forgery. As a result a considerable number of methods have been developed in view to detect different forms of copy-move forgerie

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25

S, Gayathri K., and Deepthi P. S. "An Overview of Copy Move Forgery Detection Approaches." Computer Science & Engineering: An International Journal 12, no. 6 (2022): 81–94. http://dx.doi.org/10.5121/cseij.2022.12609.

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Images have greater expressive power than any other forms of documents. With the Internet, images are widespread in several applications. But the availability of efficient open-source online photo editing tools has made editing these images easy. The fake images look more appealing and original than the real image itself, which makes them indistinguishable and hence difficult to detect. The authenticity of digital images like medical reports, scan images, financial data, crime evidence, legal evidence, etc. is of high importance. Detecting the forgery of images is therefore a major research ar

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26

Mahdi, Marwa Emad, and Nada Hussein M. Ali. "Copy Move Image Forgery Detection using Multi-Level Local Binary Pattern Algorithm." Journal of Engineering 30, no. 06 (2024): 141–57. http://dx.doi.org/10.31026/j.eng.2024.06.09.

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Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to sel

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27

Nathalie Diane, Wandji Nanda, Sun Xingming, and Fah Kue Moise. "A Survey of Partition-Based Techniques for Copy-Move Forgery Detection." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/975456.

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A copy-move forged image results from a specific type of image tampering procedure carried out by copying a part of an image and pasting it on one or more parts of the same image generally to maliciously hide unwanted objects/regions or clone an object. Therefore, detecting such forgeries mainly consists in devising ways of exposing identical or relatively similar areas in images. This survey attempts to cover existing partition-based copy-move forgery detection techniques.

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28

Naincy and 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, no. 6 (2015): 33–38. http://dx.doi.org/10.53555/nncse.v2i6.445.

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Nowadays the demand of digital images in various application areas is increasing and thus it is becoming important to ensure the authenticity of images. Due to easy availability of various image editing tools, continuous manipulations are done to create fake or forged images. Although various techniques like copy-move, splicing, resampling etc. for image forgery are present but copy move image forgery has received significant attention these days. Thus the focus of this paper is on copy-move image forgery detection techniques. We have presented a review of commonly used copy move image forgery

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29

Naincy and 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, no. 9 (2015): 01–06. http://dx.doi.org/10.53555/nncse.v2i9.441.

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Nowadays the demand of digital images in various application areas is increasing and thus it is becoming important to ensure the authenticity of images. Due to easy availability of various image editing tools, continuous manipulations are done to create fake or forged images. Although various techniques like copy-move, splicing, resampling etc. for image forgery are present but copy move image forgery has received significant attention these days. Thus the focus of this paper is on copy-move image forgery detection techniques. We have presented a review of commonly used copy move image forgery

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30

Fu, Guiwei, Yujin Zhang, and Yongqi Wang. "Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering." Applied Sciences 13, no. 13 (2023): 7528. http://dx.doi.org/10.3390/app13137528.

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Image copy-move forgery is a common simple tampering technique. To address issues such as high time complexity in most copy-move forgery detection algorithms and difficulty detecting forgeries in smooth regions, this paper proposes an image copy-move forgery detection algorithm based on fused features and density clustering. Firstly, the algorithm combines two detection methods, speeded up robust features (SURF) and accelerated KAZE (A-KAZE), to extract descriptive features by setting a low contrast threshold. Then, the density-based spatial clustering of applications with noise (DBSCAN) algor

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Deependra, Kumar Shukla, Bansal Abhishek, and Singh Pawan. "Performance analysis of various copy-move forgery detection methods." i-manager's Journal on Digital Signal Processing 10, no. 2 (2022): 1. http://dx.doi.org/10.26634/jdp.10.2.19181.

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Analyzing digital images to reveal modifications is called image forensics. Digital images are now becoming incredibly popular due to the availability of several inexpensive image-capturing gadgets. These images are frequently altered, either unintentionally or intentionally, which causes the image to convey false information. Since digital images are frequently utilized as evidence in court proceedings, media, and for preserving visual records, approaches to detecting forgeries in these images should be designed. This paper thoroughly analyzes several image forgery detection strategies, inclu

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32

Kashyap, Abhishek, Megha Agarwal, and Hariom Gupta. "Detection of copy-move image forgery using SVD and cuckoo search algorithm." International Journal of Engineering & Technology 7, no. 2.13 (2018): 79. http://dx.doi.org/10.14419/ijet.v7i2.13.11604.

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Copy-move Copy move forgery (CMF) is one of the straightforward strategies to create forged images. To detect this kind of forgery one of the widely used method is single value decomposition (SVD). Few methods based on SVD are most acceptable but some methods are less acceptable because these methods highly depend on those parameters value, which is manually selected depending upon the tampered images. For different images, we require different parameter values. In this paper, we have proposed a novel method, which uses both copy-move forgery detection using SVD and Cuckoo search (CS) algorith

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33

Kaur, Amanpreet, and Richa Sharma. "Copy-Move Forgery Detection using DCT and SIFT." International Journal of Computer Applications 70, no. 7 (2013): 30–34. http://dx.doi.org/10.5120/11977-7847.

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34

Lyu, Qiyue, Junwei Luo, Ke Liu, Xiaolin Yin, Jiarui Liu, and Wei Lu. "Copy Move Forgery Detection based on double matching." Journal of Visual Communication and Image Representation 76 (April 2021): 103057. http://dx.doi.org/10.1016/j.jvcir.2021.103057.

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35

K.Sarode, Tanuja, and Naveen Vaswani. "Copy-Move Forgery Detection using Orthogonal Wavelet Transforms." International Journal of Computer Applications 88, no. 8 (2014): 41–45. http://dx.doi.org/10.5120/15375-3966.

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36

S., Aspira, and Vikas Maheshkar. "Double Block-based Improved Copy-Move Forgery Detection." International Journal of Computer Applications 182, no. 10 (2018): 36–44. http://dx.doi.org/10.5120/ijca2018917719.

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37

GULIVINDALA, SURESH, and SRINIVASA RAO CHANAMALLU. "PERFORMANCE ANALYSIS OF COPY-MOVE FORGERY DETECTION TECHNIQUES." i-manager’s Journal on Image Processing 6, no. 1 (2019): 38. http://dx.doi.org/10.26634/jip.6.1.15925.

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38

Wang, Huan, and Hongxia Wang. "Perceptual Hashing-Based Image Copy-Move Forgery Detection." Security and Communication Networks 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6853696.

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This paper proposes a blind authentication scheme to identify duplicated regions for copy-move forgery based on perceptual hashing and package clustering algorithms. For all fixed-size image blocks in suspicious images, discrete cosine transform (DCT) is used to obtain their DCT coefficient matrixes. Their perceptual hash matrixes and perceptual hash feature vectors are orderly addressed. Moreover, a package clustering algorithm is proposed to replace traditional lexicographic order algorithms for improving the detection precision. Similar blocks can be identified by matching the perceptual ha

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39

Kumar, Tarun, and Gourav Khurana. "Copy move image forgery detection using cuckoo search." International Journal of Advanced Intelligence Paradigms 1, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijaip.2018.10021468.

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40

Jian Li, Xiaolong Li, Bin Yang, and Xingming Sun. "Segmentation-Based Image Copy-Move Forgery Detection Scheme." IEEE Transactions on Information Forensics and Security 10, no. 3 (2015): 507–18. http://dx.doi.org/10.1109/tifs.2014.2381872.

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41

Kumar Y, L. V. Santosh, Ch Sravani, and K. S. Ravi Kumar. "Copy Move Image Forgery Detection using Wavelet transform." International Journal of Engineering Trends and Technology 47, no. 4 (2017): 217–21. http://dx.doi.org/10.14445/22315381/ijett-v47p235.

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42

王, 珺斌. "Copy-Move Forgeries Detection Based on SIFT Algorithm." Computer Science and Application 05, no. 07 (2015): 255–63. http://dx.doi.org/10.12677/csa.2015.57033.

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Kumar, Tarun, and Gourav Khurana. "Copy-move image forgery detection using cuckoo search." International Journal of Advanced Intelligence Paradigms 23, no. 3/4 (2022): 357. http://dx.doi.org/10.1504/ijaip.2022.126696.

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44

Yang, Fan, Jingwei Li, Wei Lu, and Jian Weng. "Copy-move forgery detection based on hybrid features." Engineering Applications of Artificial Intelligence 59 (March 2017): 73–83. http://dx.doi.org/10.1016/j.engappai.2016.12.022.

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Zhu, Ye, Xuanjing Shen, and Haipeng Chen. "Copy-move forgery detection based on scaled ORB." Multimedia Tools and Applications 75, no. 6 (2015): 3221–33. http://dx.doi.org/10.1007/s11042-014-2431-2.

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46

Hegazi, Aya, Ahmed Taha, and Mazen Mohamed Selim. "Copy-Move Forgery Detection Based on Automatic Threshold Estimation." International Journal of Sociotechnology and Knowledge Development 12, no. 1 (2020): 1–23. http://dx.doi.org/10.4018/ijskd.2020010101.

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Recently, users and news followers across websites face many fabricated images. Moreover, it goes far beyond that to the point of defaming or imprisoning a person. Hence, image authentication has become a significant issue. One of the most common tampering techniques is copy-move. Keypoint-based methods are considered as an effective method for detecting copy-move forgeries. In such methods, the feature extraction process is followed by applying a clustering technique to group spatially close keypoints. Most clustering techniques highly depend on the existence of a specific threshold to termin

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Park, Jun Young, Tae An Kang, Yong Ho Moon, and Il Kyu Eom. "Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram." Symmetry 12, no. 4 (2020): 492. http://dx.doi.org/10.3390/sym12040492.

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Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we present an improved region duplication detection algorithm based on the keypoints. The proposed algorithm utilizes the scale invariant feature transform (SIFT) and the reduced local binary pattern (LBP) histogram. The LBP values with 256 levels are obtained from the local window centered at the keypoin

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48

Pandey, Ramesh Chand, Sanjay Kumar Singh, and K. K. Shukla. "Passive Copy- Move Forgery Detection Using Speed-Up Robust Features, Histogram Oriented Gradients and Scale Invariant Feature Transform." International Journal of System Dynamics Applications 4, no. 3 (2015): 70–89. http://dx.doi.org/10.4018/ijsda.2015070104.

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Copy-Move is one of the most common technique for digital image tampering or forgery. Copy-Move in an image might be done to duplicate something or to hide an undesirable region. In some cases where these images are used for important purposes such as evidence in court of law, it is important to verify their authenticity. In this paper the authors propose a novel method to detect single region Copy-Move Forgery Detection (CMFD) using Speed-Up Robust Features (SURF), Histogram Oriented Gradient (HOG), Scale Invariant Features Transform (SIFT), and hybrid features such as SURF-HOG and SIFT-HOG.

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Tin, Hlaing Htake Khaung. "Performance Evaluation of Local Binary Patterns LBP for Copy-Move Forgery Detection in Digital Images: A Comparative Study." International Journal of Research and Innovation in Applied Science VIII, no. IV (2023): 195–202. http://dx.doi.org/10.51584/ijrias.2023.8421.

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Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1

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50

Shah, Tawheed Jan, and M. Tariq Banday. "Passive Copy-Move Image Forgery Detection Techniques: A Study." solidstatetechnology 64, no. 2 (2021): 3293–304. https://doi.org/10.5281/zenodo.4802888.

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Due to the tremendous technological development in the digital world, there is a proliferation in the popularity of digital images in all spheres of human life. However, the introduction of state of the art Digital Image-Editing Software packages such as Pic Monkey, Adobe Lightroom, Corel PaintShop, Skylum Luminar, etc. have made image forgery non-observable and much easier than earlier times. Thus, there is a need for image authentication and forgery detection. This paper presents the active and passive image forgery detection techniques in use and then draws a comparative study of several ex

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