Journal articles: 'COPY-MOVE FORGERY'

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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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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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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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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 (June 28, 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 on three datasets (MICC-600, GRIP, and CASIA 2) to demonstrate the better performance in forgery classification and localization of the proposed method in comparison with state-of-the-art methods. Further, in copy-move localization, the source and target regions are explicitly specified.

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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 image forgery using a Convolutional Neural Network (CNN) with three different models i.e. ELA (Error Level Analysis), VGG16 and VGG19. The proposed method applies the pre-processing technique to obtain the images at a particular compression rate. These images are then utilized to train the model and further the images are classified as authentic or forged. The paper also presents the experimental results of the proposed method and performance evaluation in terms of accuracy.

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

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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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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 (September 1, 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 as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, forgers utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. This research paper illustrates recent issues in the techniques of forgery detection and proposes a advanced copy–move forgery detection scheme using adaptive over-segmentation and feature point matching. The proposed scheme integrates both block-based and key point-based forgery detection methods.

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

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

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Fu, Guiwei, Yujin Zhang, and Yongqi Wang. "Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering." Applied Sciences 13, no. 13 (June 26, 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) algorithm removes mismatched pairs and reduces false positives. To improve the accuracy of forgery localization, the algorithm uses the original image and the image transformed by the affine matrix to compare similarities in the same position in order to locate the forged region. The proposed method was tested on two datasets (Ardizzone and CoMoFoD). The experimental results show that the method effectively improved the accuracy of forgery detection in smooth regions, reduced computational complexity, and exhibited strong robustness against post-processing operations such as rotation, scaling, and noise addition.

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

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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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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 precise is realized. Specifically, in order to further exactly extract the forged regions and improve performance of the model, a two-level local search algorithm is designed in the refinement stage. In the first level, the image blocks are used as search units for feature matching, and the second level is to refine the edge of the region at pixel level. The method maintains a good balance between the complexity and effectiveness of forgery detection, and the experimental results show that it has a better detection effect than the traditional interest-based copy and move forgery detection method. In addition, CFM method has high robustness on postprocessing operations, such as scaling, rotation, noise, and JPEG compression.

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Vaishnavi, D., D. Mahalakshmi, and Venkata Siva Rao Alapati. "Visual Feature Based Image Forgery Detection." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 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 positive rate are estimated and also comparative study is carried out by previous existing methods.

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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 (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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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 (August 14, 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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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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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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Amiri, Ehsan, Ahmad Mosallanejad, and Amir Sheikhahmadi. "Copy-Move Forgery Detection Using an Equilibrium Optimization Algorithm (CMFDEOA)." Statistics, Optimization & Information Computing 11, no. 3 (April 20, 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 image converts to a grayscale. Then, with the help of a discrete cosine transform (DCT) algorithm, it is taken to the signal domain. With the help of discrete wavelet transform (DWT), its appropriate properties are introduced. In the next step, the image is divided into blocks of equal size. Then the similarity search is performed with the help of an equilibrium optimization algorithm and a suitable proportion function. Copy-move forgery detection using the Equilibrium Optimization Algorithm (CMFDEOA) can find areas of forgery with an accuracy of about 86.21% for the IMD data set and about 83.98% for the MICC-F600 data set.

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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 (March 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 fastest modes of detection which will increase the execution speed of GLCM. BRISK even processes scaled and rotated images. Then the Principal Component Analysis (PCA) algorithm is applied in the final phase of detection will remove any unrequited element from the scene and highlights the concerned forged area.

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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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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 proposed and experimental result will be given and it shows the effectiveness and accurateness of proposed methodology.

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

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

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

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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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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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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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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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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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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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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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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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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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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 (September 16, 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 forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN.

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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 (May 9, 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-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

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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 (March 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 methods of image forgery are available. Most of these methods suffer from the problem of producing false matches as false positives in flat regions. This paper presents an algorithm of the Copy-Move forgery detection using the SIFT algorithm with an effective method to remove the false positives by rejecting all key-points in matches list that own a neighbor less than the threshold. The accuracy of the proposed algorithm was 95 %. The experimental results refer that the proposed method of false positives removing can remove false matches accurately and quickly.

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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 (July 29, 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 matching the small blocks of size b b. The program is tested for 45 images of mixed image file formats by considering block sizes 2, 4, 6, 8, 10, 12, 14, and 16. It is observed from the experimental results that the proposed algorithm can detect copy-move image forgery in TIF, BMP and PNG image formats only. Results reveal that as the block size increases, execution time (time taken by CPU to display output) also increases but the number of detected forged images increases till block size 10 and attains saturation thereafter. Consequently block size should be set to 10 for getting good results in terms of less execution time.

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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 (July 15, 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 feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods.

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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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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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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 (June 29, 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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Ulutas, Guzin, and Gul Muzaffer. "A New Copy Move Forgery Detection Method Resistant to Object Removal with Uniform Background Forgery." Mathematical Problems in Engineering 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/3215162.

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Users transfer large number of images everyday over the Internet. Easy to use commercial and open source image editing tools have made intactness of images questionable. Passive methods have been proposed in the literature to determine authenticity of images. However, a specific type of forgery called “Object Removal with uniform Background forgery” becomes a problem for keypoint based methods in the literature. In this paper, we proposed an effective copy move forgery detection technique. The method uses AKAZE features and nonlinear scale space for detection of copied/pasted regions. The proposed method detects “Object Removal with uniform Background” and “Replication” types of forgeries with high precision compared to similar works. Experimental results also indicate that the method yields better discriminative capability compared to others even if forged image has been rotated, blurred, AWGN added, or compressed by JPEG to hide clues of forgery.

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Islam, Mohammad Manzurul, Gour Karmakar, Joarder Kamruzzaman, and Manzur Murshed. "A Robust Forgery Detection Method for Copy–Move and Splicing Attacks in Images." Electronics 9, no. 9 (September 12, 2020): 1500. http://dx.doi.org/10.3390/electronics9091500.

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Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy–move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors’ physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.

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Chen, Likai, Wei Lu, and Jiangqun Ni. "An Image Region Description Method Based on Step Sector Statistics and its Application in Image Copy-Rotate/Flip-Move Forgery Detection." International Journal of Digital Crime and Forensics 4, no. 1 (January 2012): 49–62. http://dx.doi.org/10.4018/jdcf.2012010104.

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A robust method for local image region feature description based on step sector statistics is proposed in this paper. The means and the standard deviations along the radial direction of the circle image region are extracted through the sector masks, and the rearrangement of these statistics makes this image region description method rotation-robust. The proposed description method is applied in the detection of copy-rotate-move forgery, and it can detect the exact rotation angle between the duplicate regions. With minor extension, the proposed description method can also be applied in the detection of copy-flip-move forgery. The experimental results show that the proposed description method can work well for the detection of copy-rotate/flip-move forgery.

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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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Saeed, Nagham Tharwat, Raghad Hazim Hamid, and Hasan Maher Ahmed Ahmed. "Copy-Move Forgery Detection Using Texture Features of Hidden Forged Regions." Technium: Romanian Journal of Applied Sciences and Technology 10 (May 13, 2023): 27–37. http://dx.doi.org/10.47577/technium.v10i.8837.

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The recent revolution in technology has not only eased our daily activities at work and home but also introduced new threats. In their daily activities, people exchange a lot of files such as text files, images, videos, etc. that can be used for a variety of purposes. One of the most common types of files is images. These kinds of files can be used to socialize people or spread knowledge among communities. Some of the exchanged images are fake or forged which can lead to the spread of misinformation, which is dangerous. This paper tries to suggest a method for image forgery detection that is copy-move-based. This means a part of the image is used to hide or change other parts in the same image. The suggested method divides an image into several blocks. The feature vectors of the blocks are extracted using a modified Gabor filter. The extracted features are, then, reduced using the principal component analysis technique. The next step is to match the blocks and extract similar ones (duplicated blocks). The findings show that the suggested method is efficient compared to other methods in the literature in terms of detection rate and false positive detection. Also, the proposed method detected forged regions of images when having a 60% of compression rate.

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Liu, Lu, Yao Zhao, Rongrong Ni, and Qi Tian. "Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features." International Journal of Digital Crime and Forensics 10, no. 4 (October 2018): 140–55. http://dx.doi.org/10.4018/ijdcf.2018100110.

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This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network (CNN)-based method to accurately localize the tampered regions, which combines color filter array (CFA) features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity.

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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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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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Journal articles on the topic 'COPY-MOVE FORGERY'

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