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Journal articles on the topic 'COPY-MOVE'

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Author: Grafiati

Published: 11 September 2023

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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 (March 2023): 1103–7. http://dx.doi.org/10.55248/gengpi.2023.32077.

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2

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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3

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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4

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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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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5

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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7

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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8

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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9

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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10

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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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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11

Singh, Ruchita, Ashish Oberoi, and Nishi Goel. "Copy Move Forgery Detection on Digital Images." International Journal of Computer Applications 98, no. 9 (July 18, 2014): 17–22. http://dx.doi.org/10.5120/17211-7437.

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12

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 can reduce computational complexity and improve the accuracy of matching at the same time. Experimental results show that the proposed method achieves better performance via comparing with the state-of-the-art copy-move forgery detection algorithms and exhibits robustness against JPEG compression, rotation, and scaling.

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13

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

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14

Ruikar, Priyanka, and Pravin Patil. "Copy Move Image Forgery Detection Using SIFT." Oriental journal of computer science and technology 9, no. 3 (December 25, 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 specific form of image forgery operation & it is considered one of the most difficult problems in that case for this a part of any image is copied & pa tested on another location of an image that may be a same or different image, to obfuscate undesirable objects in the scene. In this paper, the method is proposed which automatically detects & identifies the duplicated regions in the image. In that process first image segmentation takes place & by identifying the local characteristics of the images (points of interest) the duplication is detected using SIFT (Scale Invariant Feature Transform).

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15

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

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16

Kaur, Gurpreet, and Rajan Manro. "Comparative Study of Copy Move Forgery Techniques." International Journal of Engineering Trends and Technology 67, no. 3 (March 25, 2019): 146–51. http://dx.doi.org/10.14445/22315381/ijett-v67i3p228.

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17

Pavlović, Aleksandra, Natasa Glišović, Ana Gavrovska, and Irini Reljin. "Copy-move forgery detection based on multifractals." Multimedia Tools and Applications 78, no. 15 (March 5, 2019): 20655–78. http://dx.doi.org/10.1007/s11042-019-7277-1.

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18

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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19

Mushtaq, Saba, and Ajaz Hussain Mir. "Image Copy Move Forgery Detection: A Review." International Journal of Future Generation Communication and Networking 11, no. 2 (March 31, 2018): 11–22. http://dx.doi.org/10.14257/ijfgcn.2018.11.2.02.

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20

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 (April 30, 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 berupa citra input dan juga ukuran blok . Keluaran dari sistem ini adalah daerah yang terdeteksi sebagai copy move atau tidak terdeteksi sama sekali beserta dengan daerah yang di duga sebagai false match.Hasil akhir dari sistim ini ditunjukkan dalam bentuk akurasi, area false match dan waktu ekseukusi. Akurasi metode Exact Match untuk blok 4 x 4, 8 x 8 dan 16 x 16 adalah lebih baik dibandingkan dengan DWT walaupun memiliki area false match yang lebih besar. Akurasi dari DWT Haar, DWT db2 dan DWT db4 tergantung dari wilayah atau daerah copy move dalam citra. Blok 4 x 4 mempunyai area false match yang lebih besar dari blok 8 x 8 dan 16 x 16. Waktu eksekusi tergantung dari besar blok, semakin besar blok semakin besar waktu eksekusi.Kata kunci—copy move forgery, Exact Match, DWT Haar, DWT DB2, DWT DB4. AbstractCopy-Move Forgery is a special type of image forgery, in which a part of a digital image is copied and pasted to another part in the same image in order to cover an important image feature. This research developed a system to detect copy move forgery in digital image. The system is intended to help the user determine whether an image is authentic or already contained a copy move object, and if the image already contains copy move object, the system can determine in which section the copy move object is located. Copy move forgery detection system discussed in this research, was developed by using Exact Match, DWT Haar, DWT db2 and DWT db4 using blocks of 4 x 4, 8 x 8 and 16 x 16. Users can use the system by using the digital image as input. The output of the system is the information about the area detected as a copy move forgery along with areas suspected of being false match.The final result is shown in the form of accuracy, the area of the false match and execution time. Based on the test results, the accuracy of Exact Match method for blocks of 4 x 4, 8 x 8 and 16 x 16 is better than the DWT, although exact match have an bigger false match area. Accuracy of DWT Haar, DWT db2 and DWT DB4 depending on the copy move area on the image. Block 4 x 4 has a false match area larger than the block 8 x 8 and 16 x 16. The execution time depends on the size of the block, the larger the block, the longer the time of execution. Keywords— Copy move forgery, Exact Match, DWT Haar, DWT db2, DWT db4

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21

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 (October 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 forgeries. Those techniques can be classified generally as block based techniques and key- point based techniques. This paper presents an extensive survey on the very recent methods developed for copy-move forgery detection.

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22

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 (July 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. SIFT and SURF image features are immune to various transformations like rotation, scaling, translation, so SIFT and SURF image features help in detecting Copy-Move regions more accurately in compared to other image features. Further the authors have detected multiple regions COPY-MOVE forgery using SURF and SIFT image features. Experimental results demonstrate commendable performance of proposed methods.

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23

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 (June 30, 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 detection techniques and the comparison of same is also showed to evaluate their performance on basis of various parameters.

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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 (September 30, 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 detection techniques and the comparison of same is also showed to evaluate their performance on basis of various parameters.

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25

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 (June 30, 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 of key-point based methods and to make copy move forgery detection more reliable. In this paper, keypoint based methods such as SIFT, SURF, ORB and theirintegrated methods are reviewed.

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26

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 (April 15, 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) algorithm. It utilizes Cuckoo search algorithm to generate customized parameter values for different tampered images, which are used in copy-move forgery detection (CMFD) under block based framework.

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27

Kaur, Amanpreet, and Richa Sharma. "Copy-Move Forgery Detection using DCT and SIFT." International Journal of Computer Applications 70, no. 7 (May 17, 2013): 30–34. http://dx.doi.org/10.5120/11977-7847.

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28

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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29

Fadhel Homady Sewan, Ayat, and Mohammed Sahib Mahdi Altaei. "Forged Copy-Move Recognition Using Convolutional Neural Network." Al-Nahrain Journal of Science 24, no. 1 (March 1, 2021): 45–56. http://dx.doi.org/10.22401/anjs.24.1.08.

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Due to the extreme robust image editing techniques, digital images are subject to multiple manipulations and decreased costs for digital camera and smart phones. Therefore, image credibility is becoming questionable, specifically when images have strong value, such as news report and insurance claims in a crime court. Therefore, image forensic methods test the integrity of the images by applying various highly technical methods set out in the literature. The present work deals with one important research module is the recognition of forged part that applied on copy move forgery images. Two datasets MICC-F2000 and CoMoFoD are used, these datasets are usually adopted in the field of interest. The module concerned with recognizing which is the source image portion and which is the target one of that already detected. Thus, the two detected tampered parts of the image are recognized the original one from them, the other is then referred as forged or tampered part. The proposed module used the buster net of three neural networks that basically adopted the principle of training by using Convolution Neural Network (CNN) to extract the most important features in the images. The first and second networks are parallel working to detect and identify areas that have been tampered with, and then display them through two masks. While the last network classifier takes a copy of these two catchers to decide which is the source image portion from the two detected ones. The achieved recognition results were about F-score 98.98% even if the forged area is rotated or scaled or both of them. Also, the recognition results of the forged image part was 98% when using images do not contributed in the training phase, which refers to that the proposed module is more confident and reliable.

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K.Sarode, Tanuja, and Naveen Vaswani. "Copy-Move Forgery Detection using Orthogonal Wavelet Transforms." International Journal of Computer Applications 88, no. 8 (February 14, 2014): 41–45. http://dx.doi.org/10.5120/15375-3966.

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31

S., Aspira, and Vikas Maheshkar. "Double Block-based Improved Copy-Move Forgery Detection." International Journal of Computer Applications 182, no. 10 (August 14, 2018): 36–44. http://dx.doi.org/10.5120/ijca2018917719.

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32

Abramova, Svetlana, and Rainer Böhme;. "Detecting Copy–Move Forgeries in Scanned Text Documents." Electronic Imaging 2016, no. 8 (February 14, 2016): 1–9. http://dx.doi.org/10.2352/issn.2470-1173.2016.8.mwsf-068.

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33

Abdalla, Younis, M. Iqbal, and Mohamed Shehata. "Convolutional Neural Network for Copy-Move Forgery Detection." Symmetry 11, no. 10 (October 14, 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-move forgery techniques is explained. The experiments show that the overall validation accuracy is 90%, with a set iteration limit.

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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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35

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 hash feature vectors in each package and its adjacent package. The experimental results show that the proposed scheme can locate irregular tampered regions and multiple duplicated regions in suspicious images although they are distorted by some hybrid trace hiding operations, such as adding white Gaussian noise and Gaussian blurring, adjusting contrast ratio, luminance, and hue, and their hybrid operations.

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36

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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37

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 (March 2015): 507–18. http://dx.doi.org/10.1109/tifs.2014.2381872.

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38

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 (May 25, 2017): 217–21. http://dx.doi.org/10.14445/22315381/ijett-v47p235.

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39

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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40

S, Gayathri K., and Deepthi P. S. "An Overview of Copy Move Forgery Detection Approaches." Computer Science & Engineering: An International Journal 12, no. 6 (December 30, 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 area. Image forgery is categorized as copy-move forgery, splicing, and retouching. In this work, a review of copy-move forgery is discussed along with the existing research on its detection and localization using both conventional and deep-learning mechanisms. The datasets used and challenges towards improving or developing novel algorithms are also presented.

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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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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 (January 8, 2015): 3221–33. http://dx.doi.org/10.1007/s11042-014-2431-2.

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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 (March 26, 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 keypoint, which are then reduced to 10 levels. For a keypoint, a 138-dimensional is generated to detect copy-move forgery. We test the proposed algorithm on various image datasets and compare the detection accuracy with those of existing methods. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested copy-move forgery detection methods. Furthermore, the proposed method exhibits a uniform detection performance for various types of test datasets.

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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 (January 2020): 1–23. http://dx.doi.org/10.4018/ijskd.2020010101.

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Abstract:

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 terminate the clustering. Determination of the most suitable threshold requires a huge amount of experiments. In this article, a copy-move forgery detection method is proposed. The proposed method is based on automatic estimation of the clustering threshold. The cutoff threshold of hierarchical clustering is estimated automatically based on clustering evaluation measures. Experimental results tested on various datasets show that the proposed method outperforms other relevant state-of-the-art methods.

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Khudhair, Zaid Nidhal, Farhan Mohamed, and Karrar A. Kadhim. "A Review on Copy-Move Image Forgery Detection Techniques." Journal of Physics: Conference Series 1892, no. 1 (April 1, 2021): 012010. http://dx.doi.org/10.1088/1742-6596/1892/1/012010.

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Gavade, Jayashree D., S. R. Chougule, and Vishwaja Rathod. "A robust passive blind copy-move image forgery detection." International Journal of Information and Computer Security 14, no. 3/4 (2021): 300. http://dx.doi.org/10.1504/ijics.2021.114707.

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Zhou, X. H., and Q. J. Shi. "Multiple Copy-Move Forgery Detection Based on Density Clustering." Pattern Recognition and Image Analysis 31, no. 1 (January 2021): 109–16. http://dx.doi.org/10.1134/s1054661821010181.

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Glumov, N. I., A. V. Kuznetsov, and V. V. Myasnikov. "THE ALGORITHM FOR COPY-MOVE DETECTION ON DIGITAL IMAGES." Computer Optics 37, no. 3 (January 1, 2013): 360–67. http://dx.doi.org/10.18287/0134-2452-2013-37-3-360-367.

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Kuznetsov, A. V., and V. V. Myasnikov. "EFFICIENT LINEAR LOCAL FEATURES BASED COPY-MOVE DETECTION ALGORITHM." Computer Optics 37, no. 4 (January 1, 2013): 489–95. http://dx.doi.org/10.18287/0134-2452-2013-37-4-489-495.

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