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Journal articles on the topic 'COPY-MOVE FORGERY'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 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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2

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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3

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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4

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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Pham, Nam Thanh, Jong-Weon Lee, and Chun-Su Park. "Structural Correlation Based Method for Image Forgery Classification and Localization." Applied Sciences 10, no. 13 (2020): 4458. http://dx.doi.org/10.3390/app10134458.

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In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions inside each cluster to classify and localize tampered images. Comprehensive experiments are conducted

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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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7

Gupta, Anil. "A New Copy Move Forgery Detection Technique using Adaptive Over-segementation and Feature Point Matching." Bulletin of Electrical Engineering and Informatics 7, no. 3 (2018): 345–49. http://dx.doi.org/10.11591/eei.v7i3.754.

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With the development of Image processing editing tools and software, an image can be easily manipulated. The image manipulation detection is vital for the reason that an image can be used as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copy-move or splicing, resampling a digital image (stretch, rotate, resize), removal

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8

Mallick, Devjani, Mantasha Shaikh, Anuja Gulhane, and 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.

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The boom of digital images coupled with the development of approachable image manipulation software has made image tampering easier than ever. As a result, there is massive increase in number of forged or falsified images that represent incorrect or false information. Hence, the issue of image forgery has become a major concern and it must be addressed with appropriate solution. Throughout the years, various computer vision and deep learning solutions have emerged with a purpose to detect forgery in case of digital images. This paper presents a novel approach to detect copy move and splicing i

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9

Zhao, Wei, Yujin Zhang, Yongqi Wang, and Shiwen Zhang. "An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis." Applied Sciences 14, no. 11 (2024): 4882. http://dx.doi.org/10.3390/app14114882.

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In audio copy-move forgery forensics, existing traditional methods typically first segment audio into voiced and silent segments, then compute the similarity between voiced segments to detect and locate forged segments. However, audio collected in noisy environments is difficult to segment and manually set, and heuristic similarity thresholds lack robustness. Existing deep learning methods extract features from audio and then use neural networks for binary classification, lacking the ability to locate forged segments. Therefore, for locating audio copy-move forgery segments, we have improved d

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10

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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11

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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12

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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13

Gupta, Anil. "A New Copy Move Forgery Detection Technique Using Adaptive Over-segementation and Feature Point Matching." Bulletin of Electrical Engineering and Informatics 7, no. 3 (2018): 345–49. https://doi.org/10.11591/eei.v7i3.754.

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With the development of Image processing editing tools and software, an image can be easily manipulated. The image manipulation detection is vital for the reason that an image can be used as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copymove or splicing, resampling a digital image (stretch, rotate, resize), removal a

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14

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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15

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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16

Mei, Fang, Tianchang Gao, and Yingda Lyu. "CF Model: A Coarse-to-Fine Model Based on Two-Level Local Search for Image Copy-Move Forgery Detection." Security and Communication Networks 2021 (May 4, 2021): 1–13. http://dx.doi.org/10.1155/2021/6688393.

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Copy-move forgery is the most predominant forgery technique in the field of digital image forgery. Block-based and interest-based are currently the two mainstream categories for copy-move forgery detection methods. However, block-based algorithm lacks the ability to resist affine transformation attacks, and interest point-based algorithm is limited to accurately locate the tampered region. To tackle these challenges, a coarse-to-fine model (CFM) is proposed. By extracting features, affine transformation matrix and detecting forgery regions, the localization of tampered areas from sparse to pre

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17

Vaishnavi, D., D. Mahalakshmi, and Venkata Siva Rao Alapati. "Visual Feature Based Image Forgery Detection." International Journal of Engineering & Technology 7, no. 4.6 (2018): 86. http://dx.doi.org/10.14419/ijet.v7i4.6.20436.

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In present days, the images are building up in digital form and which may hold essential information. Such images can be voluntarily forged or manipulated using the image processing tools to abuse it. It is very complicated to notice the forgery by naked eyes. In particular, the copy move forgery is enormously demanding one to expose. Hence, this paper put forwards a method to determine the copy move forgery by extracting the visual feature called speed up robust features (SURF). In the direction to quantitatively analyze the performance, the metrics namely false positive rate and true positiv

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18

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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19

K, Sudhakar, and Dr Subhash Kulkarni. "Performance Evaluation of Distance Metric for Copy Move Forgery Detection." Journal of University of Shanghai for Science and Technology 23, no. 08 (2021): 457–61. http://dx.doi.org/10.51201/jusst/21/08412.

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This paper presents the performance evaluation of various distance metric in copy move forger detection algorithms. The choice of distance metric affects the detection speed. The proposed approach is tested over 9 different distance metrics. The experimental results found indicate the choice of distance metric has a considerable impact on forgery detection speed.

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20

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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21

Rony Sina, Derwin, and Agus Harjoko. "Deteksi Copy Move Forgery Pada Citra Menggunakan Exact Match, DWT Haar dan Daubechies." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 6, no. 1 (2016): 25. http://dx.doi.org/10.22146/ijeis.10768.

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AbstrakCopy-Move Forgery adalah satu tipe gangguan citra digital, di mana bagian dari citra dicopy dan dipastekan ke bagian lain dalam citra yang sama untuk menutupi fitur citra yang penting. Pada penelitian ini, dibangun sistem pendeteksi copy move forgery pada citra. Sistem ini dimaksudkan untuk membantu user mengetahui bahwa suatu citra masih asli atau sudah terdapat copy move dan dibagian mana terjadinya copy move tersebut. Sistem ini dibangun dengan menggunakan metode Exact Match, DWT Haar, DWT DB2 dan DWT DB4 dengan menggunakan blok 4 x 4, 8 x 8 dan 16 x 16. Masukan dari sistem ini berup

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22

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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23

Amiri, Ehsan, Ahmad Mosallanejad, and Amir Sheikhahmadi. "Copy-Move Forgery Detection Using an Equilibrium Optimization Algorithm (CMFDEOA)." Statistics, Optimization & Information Computing 11, no. 3 (2023): 677–84. http://dx.doi.org/10.19139/soic-2310-5070-1511.

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Image forgery detection is a new challenge. One type of image forgery is a copy-move forgery. In this method, part of the image is copied and placed at the most similar point. Given the existing algorithms and processing software, identifying forgery areas is difficult and has created challenges in various applications. The proposed method based on the Equilibrium Optimization Algorithm (EOA) helps image forgery detection by finding forgery areas. The proposed method includes feature detection, image segmentation, and detection of forgery areas using the EOA algorithm. In the first step, the i

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Nirosha, Kandukuri. "Digital Image Forgery Detection Using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 456–65. https://doi.org/10.22214/ijraset.2025.67285.

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Digital images are a main source of shared information in social media. Digital image forgery has become a growing concern with the advancement of image editing tools, leading to the spread of misleading and manipulated content. Detecting such forgeries is crucial for ensuring the authenticity and reliability of digital images. Various digital image forgery detection techniques are tied to detecting only one type of forgery, such as image splicing or copy-move it is not applied in real life. To enhance digital image forgery detection using deep learning techniques via transfer learning is unco

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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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Singh, Amarpreet, and Sanjogdeep Singh. "Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries." Journal of Image and Graphics 11, no. 1 (2023): 82–90. http://dx.doi.org/10.18178/joig.11.1.82-90.

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With advancement in technology, especially in imaging field, digital image forgery has increased a lot nowadays. In order to counter this problem, many forgery detection techniques have been developed from time to time. For rapid and accurate detection of forged image, a novel hybrid technique is used in this research work that implements Gray Level Co-occurrence Matrix (GLCM) along with Binary Robust Invariant Scalable Keypoints (BRISK). GLCM significantly extracts key attributes from an image efficiently which will help to increase the detection accuracy. BRISK is known to be one of the 3 fa

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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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Liu, Bo, and Chi Man Pun. "HSV Based Image Forgery Detection for Copy-Move Attack." Applied Mechanics and Materials 556-562 (May 2014): 2825–28. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2825.

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As the great development of digital photography and relevant post-processing technology, digital image forgery becomes easily in terms of operating thus may be improperly utilized in news photography in which any forgery is strictly prohibited or the other scenario, for instance, as an evidence in the court. Therefore, digital image forgery detection technique is needed. In this paper, attention has been focused on copy-move forgery that one region is copied and then pasted onto other zones to create duplication or cover something in an image. A novel method based on HSV color space feature is

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S., Uma*1 &. Dr. P. D. Sathya2. "A DETAILED REVIEW OF COPY-MOVE FORGERY DETECTION IN DIGITAL IMAGE." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 1 (2019): 38–49. https://doi.org/10.5281/zenodo.2537823.

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Today it became very hard to trust the digital photographs; these have to be verified for their originality. Recently a BBC News article says <strong>that <em>“</em></strong><strong><em>Eduardo Martins fooled journalists and picture editors by making slight alterations to the images, such as inverting them, just enough to elude software that scans pictures for plagiarism</em></strong><strong><em> “</em></strong>. To address these issues, in this article we are going to discuss the  image forensic concepts. Two Different techniques are used to create forgery in the digital imag

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Abdalla, Younis, M. Tariq Iqbal, and Mohamed Shehata. "Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network." Information 10, no. 9 (2019): 286. http://dx.doi.org/10.3390/info10090286.

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The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a f

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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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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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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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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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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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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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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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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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Kaur, Gurpreet, and Rajan Manro. "Comparative Study of Copy Move Forgery Techniques." International Journal of Engineering Trends and Technology 67, no. 3 (2019): 146–51. http://dx.doi.org/10.14445/22315381/ijett-v67i3p228.

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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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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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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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Shawkat Ibrahim, Zainab, and Taha Mohammed Hasan. "Copy-Move Image Forgery Detection Using Deep Learning Approaches: An Abbreviated Survey." Bilad Alrafidain Journal for Engineering Science and Technology 4, no. 1 (2025): 137–54. https://doi.org/10.56990/bajest/2025.040112.

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Images play a fundamental role in digital media, and altering digital images can present a significant risk since it contributes to disseminating false information. The rapid advancement of technology in digital image forensics has significantly improved the quality of forged images to the extent that many forgeries are now indistinguishable. Digital image authenticity and reliability are becoming more significant as evidence. Some people invalidate photos by adding or removing sections. Therefore, image forgery detection and localization are crucial. Image manipulation techniques have made th

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Vaishali Sharma. "Ensuring Visual Integrity: Deep Learning-Based Solutions for Authentic Image Forgery Detection." Journal of Electrical Systems 20, no. 11s (2024): 3491–508. https://doi.org/10.52783/jes.8129.

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Digital image manipulation has become increasingly prevalent with the advancement of image editing tools, posing significant challenges in digital forensics. Detecting and localizing two common types of image forgery copy-move forgery and spliced image forgery remains a critical task. This paper proposes an approach that leverages EfficientFormer for forgery detection and BCU-Net with a spatial attention mechanism for localization. EfficientFormer is used to classify images as forged or original, while BCU-Net precisely identifies and localizes the forged regions. The study utilizes well-known

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Arora, Priyanka, and Derminder Singh. "Copy Move Image Forgery Detection with Exact Match Block Based Technique." Oriental journal of computer science and technology 12, Issue 3 (2019): 123–31. http://dx.doi.org/10.13005/ojcst12.03.07.

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Digital images are a momentous part of today’s digital communication. It is very easy to manipulate digital images for hiding some useful information by image rendering tools such as Adobe Photoshop, Microsoft Paint etc. The common image forgery which is easy to carry out is copy-move in which some part of an image is copied and pasted on another part of the same image to hide the important information. In this paper we propose an algorithm to spot the copy-move forgery based on exact match block based technique. The algorithm works by matching the regions in image that are equivalent by match

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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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Zhao, Kaiqi, Xiaochen Yuan, Zhiyao Xie, Yan Xiang, Guoheng Huang, and Li Feng. "SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection." Sensors 23, no. 14 (2023): 6430. http://dx.doi.org/10.3390/s23146430.

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With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts featur

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Patel, Mili. "Copy-Move Forgery Detection in Medical Images Using Handcrafted Features." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1520–24. http://dx.doi.org/10.22214/ijraset.2023.56677.

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Abstract: In the realm of medical imaging, the authenticity and integrity of images are paramount for accurate diagnosis and treatment planning. Copy-move forgery, a prevalent form of image tampering, poses a significant threat to the reliability of medical images. This research project focuses on the development and implementation of a robust copy-move forgery detection system tailored specifically for medical images. The proposed methodology leverages handcrafted features, extracting distinctive characteristics from the images to detect instances of forgery. Through a meticulous process of f

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Mahdi, Muthana Salih, and Saad N. Alsaad. "False Matches Removing in Copy-Move Forgery Detection Algorithms." Al-Mustansiriyah Journal of Science 31, no. 1 (2020): 47. http://dx.doi.org/10.23851/mjs.v31i1.748.

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Today the technology age is characterized by spreading of digital images. The most common form of transfer the information in magazines, newspapers, scientific journals and all types of social media. This huge use of images technology has been accompanied by an evolution in editing tools of image processing which make modifying and editing an image is very simple. Nowadays, the circulation of such forgery images, which distort the truth, has become common, intentionally or unintentionally. Nowadays many methods of copy-move forgery detection which is one of the most important and popular metho

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KUMARI, Manish, and Rajesh SHARMA. "Comparative Study of Various Forgery Detection Approach for Image Processing." International Journal of Information Security and Cybercrime 10, no. 1 (2021): 18–26. http://dx.doi.org/10.19107/ijisc.2021.01.02.

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Considering the availability of powerful image analysis and editing tools, digital images are easy to change and transfer. This is necessary to link or erase any important elements from any image without escaping any valid visible signs of interfering. Including its real-life apps in different areas, the copy move forgery method is analyzed in depth. Implementation phases for the detection of image forgery are also clarified, accompanied by various approaches using copy move forgery approach.

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