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Journal articles on the topic 'Digital forgery'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Sarhan, M. Musa1. "DIGITAL FORGERY." International Journal of Advances In Scientific Research and Engineering (IJASRE) 3, no. 4 (2017): 26–29. https://doi.org/10.5281/zenodo.581732.

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<em>Forgery is the criminal act that provides misleading information about a product or service. It is the process of making, adapting, or imitating documents or objects with the intent to deceive. Digital forgery (or digital tampering) is the process of manipulating documents or images for the intent of financial, social or political gain. This paper provides a brief introduction to the digital forgery.</em>

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Liang, Yu, Yadong Yu, Yina Wang, Dunjun Li, and Zejiong Zhou. "Counterfeiting in Depth Synthesis based on Digital Watermarking." Frontiers in Computing and Intelligent Systems 5, no. 3 (2023): 100–106. http://dx.doi.org/10.54097/fcis.v5i3.13998.

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The purpose of this paper is to discuss and apply digital watermarking technology to solve the forgery problem in depth synthesis. With the rapid development of deep synthesis technology and its application in various fields, it is particularly important to protect the authenticity and integrity of digital content. Based on the understanding of digital watermarking, this paper explores an experimental design, which uses watermarking embedding and extraction algorithms and forgery detection technology to solve the problem of deep forgery, protect the copyright, integrity and anti-copy of digita

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

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

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Hussien, Nadheer Younus, Rasha O. Mahmoud, and Hala Helmi Zayed. "Deep Learning on Digital Image Splicing Detection Using CFA Artifacts." International Journal of Sociotechnology and Knowledge Development 12, no. 2 (2020): 31–44. http://dx.doi.org/10.4018/ijskd.2020040102.

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Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images

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

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

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

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

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Mr. M. Sasikanth, Ms M. Sri Siva Sravani Devi, Ms K. Bindu Madhavi, Mr V. Vigneswara Rao, and Mr. J. Prudhvi. "Digital Image Forgery Detection using Hierarchical Learning." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 1186–94. https://doi.org/10.36893/jnao.2025.v16i01.0139.

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Digital image forgery has emerged as a critical challenge in the digital era, with deep learning techniques offering promising solutions for detecting manipulated images. This paper presents a comprehensive approach to image forgery detection using convolutional neural networks (CNNs) and deep learning models such as ResNet, EfficientNet, and Vision Transformers (ViTs). The methodology involves dataset preprocessing, feature extraction, and classification of forged versus authentic images. Performance evaluation metrics such as accuracy, precision, recall, and F1-score are used to compare diff

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SU, YUTING, JING ZHANG, YU HAN, JING CHEN, and QINGZHONG LIU. "EXPOSING DIGITAL VIDEO LOGO-REMOVAL FORGERY BY INCONSISTENCY OF BLUR." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 07 (2010): 1027–46. http://dx.doi.org/10.1142/s0218001410008317.

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A novel approach for detecting video logo-removal forgery is proposed by measuring inconsistency of blur. Our approach is based on the assumption that if a digital video undergoes logo-removal forgery; the blurriness of the forged region is expected to be different as compared to the nontampered parts of the video. Blurriness is first estimated by analyzing the spatial and temporal statistical property of logo areas, and suspicious areas are roughly located; then features are extracted and a fine classification is implemented by applying support vector machine (SVM) to extract features. If the

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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 (2018): 345–49. http://dx.doi.org/10.11591/eei.v7i3.754.

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

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

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

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Pandey, Shivam, Aditya ., Seema Jain, and Usha Dhankar. "IMAGE FORGERY DETECTION." International Journal of Engineering Applied Sciences and Technology 8, no. 2 (2023): 160–63. http://dx.doi.org/10.33564/ijeast.2023.v08i02.022.

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In the digital age, the ability to manipulate digital images has become increasingly sophisticated, making it challenging to detect and distinguish between authentic and forged images. Image forgery detection is an active and crucial area of research, with various methods and techniques being proposed to detect manipulated images. This paper provides a survey of current methods and techniques for image forgery detection. We begin by introducing the different types of image forgeries and their characteristics. We then discuss the various methods for detecting forgeries, including statistical me

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Prof. D. D. Pukale, Prof. V. D. Kulkarni, Julekha Bagwan, Pranali Jagadale, Sanjivani More, and Renuka Sarmokdam. "Image Forgery Detection Using Deep Learning." International Research Journal on Advanced Engineering and Management (IRJAEM) 6, no. 07 (2024): 2248–58. http://dx.doi.org/10.47392/irjaem.2024.0327.

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Image forgery is a big problem in digital media, making it important to have strong detection methods to fight misinformation and keep trust in visual content. In this project, we introduce an advanced image forgery detection system using VGG16, a powerful convolutional neural network, and Error Level Analysis (ELA) algorithms. Our goal is to create an efficient and accurate system that can identify real images from fake ones, especially focusing on detecting splicing and copy-move forgeries. By examining pixel intensities and patterns, our system can accurately find tampered areas, improving

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Gaharwar, Gaurav, V. V. Nath, and Raina Gaharwar. "Neuro-Fuzzy Based First Responder for Image Forgery Identification." Oriental journal of computer science and technology 9, no. 1 (2016): 12–16. http://dx.doi.org/10.13005/ojcst/901.03.

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Image forgery is always been sought after field of digital forensics, as it becomes very convenient now to edit/forge any image with many desktop based and online tools available. Also, to prove authenticity of any image in the court of law, there is a need of algorithm which can used to check forgery of any image, irrespective of its forgery type. Proposed model in the paper aims to provide neuro-fuzzy based algorithm which utilizes capabilities of best algorithms for each type and provides accurate result about the forgery in the image. It also provides analysis about type of forgery in the

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Parashar, Amrita, Dr Arvind Kumar Upadhyay, and Dr Kamlesh Gupta. "A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image." ECS Transactions 107, no. 1 (2022): 11791–98. http://dx.doi.org/10.1149/10701.11791ecst.

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Image manipulation can cause many ethical, economical, and political issues for everyone. It can be used to create fake information, fake ids, fake online profiles, fake news. A proper technique for image detection and verification is thus, a hot research area, which helps the affected people to overcome the forgery attacks. Due to the increased interaction among people through social networking sites, image forgery is prevalent and it is an essential activity to detect any forged image. This paper’s main objective is to identify fraud or tampered images effectively. By using the methods of co

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Hosny, Khalid M., Akram M. Mortda, Nabil A. Lashin, and Mostafa M. Fouda. "A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network." Applied Sciences 13, no. 3 (2023): 1272. http://dx.doi.org/10.3390/app13031272.

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Recently, digital images have been considered the primary key for many applications, such as forensics, medical diagnosis, and social networks. Image forgery detection is considered one of the most complex digital image applications. More profoundly, image splicing was investigated as one of the common types of image forgery. As a result, we proposed a convolutional neural network (CNN) model for detecting splicing forged images in real-time and with high accuracy, with a small number of parameters as compared with the recently published approaches. The presented model is a lightweight model w

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Gornale, Shivanand S., Gayatri Patil, and Rajkumar Benne. "Document Image Forgery Detection Using RGB Color Channel." Transactions on Machine Learning and Artificial Intelligence 10, no. 5 (2022): 1–14. http://dx.doi.org/10.14738/tmlai.105.13126.

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Using advanced digital technologies and photo editing software, document images, such as typed and handwritten documents, can be manipulated in a variety of ways. The most common method of document forgery is adding or removing information. As a result of the changes made to document images, there is misinformation and misbelief in document images. Forgery detection with multiple forgery operations is challenging issue. As a result, special consideration is given in this work to the ten-class problem, in which a text can be altered using multiple forgery types. The characteristics are computed

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Siddiqi, Muhammad Hameed, Khurshed Asghar, Umar Draz, et al. "Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns." Security and Communication Networks 2021 (September 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/4270776.

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With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detect

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Nagarathna C R, Jayasri A, Chandana S, and Amrutha A. "Identification of Image Forgeries using Machine Learning - A Review." Journal of Innovative Image Processing 5, no. 3 (2023): 323–36. http://dx.doi.org/10.36548/jiip.2023.3.007.

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Forgery in images is the manipulation of digital images using techniques like copy-move, splicing, removal of parts of image. Image forgery detection is a crucial task in digital image processing field. The growth and use of digital images in various industries such as forensics, journalism and scientific research has increased the number of manipulated and forged images. New and advanced editing tools and techniques are capable of easily manipulating images without leaving traces, which can lead to negative impact for individuals and society. Therefore, the need for reliable and efficient for

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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 (2015): 33–38. http://dx.doi.org/10.53555/nncse.v2i6.445.

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

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Naincy and Ashok Kumar Bathla. "Comparative Study and Survey on Copy Move Image Forgery Detection Approaches." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 9 (2015): 01–06. http://dx.doi.org/10.53555/nncse.v2i9.441.

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

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Huda Abdulaali Abdulbaqi. "Advanced Software Techniques for Detecting Digital Image Manipulation." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 326–38. https://doi.org/10.52783/jisem.v10i36s.6423.

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It is necessary to detect forgery of digital images in order to maintain their integrity. In this paper an attempt is made to solve the problem of copy-move forgery detection which is the most common form of image manipulation. We propose two new methods for detecting duplicated regions which are based on the texture and statistical features. The first technique is based on the classical SIFT (Scale-Invariant Feature Transform) which is a keypoint based method; the second one is based on the integration of SIFT into deep learning techniques and software solutions. We used the MICC-F600 databas

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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 (2022): 69. http://dx.doi.org/10.3390/jimaging8030069.

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The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy–move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-ban

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Bae, Yong-Yeol, Dae-Jea Cho, and Ki-Hyun Jung. "Visual Complexity in Korean Documents: Toward Language-Specific Datasets for Deep Learning-Based Forgery Detection." Applied Sciences 15, no. 8 (2025): 4319. https://doi.org/10.3390/app15084319.

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Recent advancements in information and communication technology have driven various organizations, including businesses, government agencies, and institutions, to digitize and manage critical documents. Document digitization mitigates spatial constraints on storage and offers significant advantages in transmission and management. However, while digitization offers many benefits, the development of image processing software has also increased the risk of forgery and manipulation of digital documents. Digital documents, ranging from everyday documents to those handled by major institutions, can

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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 i

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

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

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Dr. Vikram S. Patil, Ms. Muskan Sayyad, Mr. Sangram Shinde, Mr. Salman Pathan, and Mr. Shreyash Nikam. "Image Forgery Detection." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 07 (2025): 2425–32. https://doi.org/10.47392/irjaem.2025.0383.

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In the digital age, the proliferation of image editing tools has made it easier than ever to manipulate images, raising concerns about the authenticity and credibility of visual content. This project focuses on the development of an effective and efficient image forgery detection system to address the growing challenges associated with digital image tampering. The proposed system leverages advanced techniques in computer vision and machine learning to detect common forms of forgeries, such as copy-move, splicing, and removal. Using feature extraction methods such as SURF, SIFT, and deep learni

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Kinjal, Ravi Sheth, and Suryakantbhai Vora Vishal. "A comparative study on image forgery-facial retouching." Bulletin of Electrical Engineering and Informatics 12, no. 2 (2023): 851~859. https://doi.org/10.11591/eei.v12i2.4481.

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Forgery with the digital images is being very easy now days due to the very advanced and open source image editing tools, software and devises which supports a high quality of resolutions. Tempering with digital documents for changing identity or sometimes for fun is increasing day by day as the era is of digital world. Detecting clues of tampering and verifying the authenticity of images is an important issues now-a-days and growing research field. The existing research in the area of digital image forgery identification is discussed here. Different types of image forgery attacks along with i

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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 (2018): 86. http://dx.doi.org/10.14419/ijet.v7i4.6.20436.

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

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Saha, Monica. "Forensic Technique for Forgery Detection and Localization in Digital Image." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2721–27. https://doi.org/10.22214/ijraset.2025.68495.

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Abstract: The widespread availability of advanced image editing software has transformed digital image forgery into an urgent issue in multimedia forensics. Traditional forgery methods—copy-move, splicing, and retouching—taint the authenticity of digital images, allowing malicious individuals to disseminate misinformation, tamper with legal evidence, and compromise digital trust. Forgery detection and localization are crucial for uses in cybersecurity, journalism, law enforcement, and digital forensics.This paper provides a systematic survey and comparative evaluation of the latest forensic me

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Jegaveerapandian, Liba Manopriya, Arockia Jansi Rani, Prakash Periyaswamy, and Sakthivel Velusamy. "A survey on passive digital video forgery detection techniques." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6324. http://dx.doi.org/10.11591/ijece.v13i6.pp6324-6334.

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Digital media devices such as smartphones, cameras, and notebooks are becoming increasingly popular. Through digital platforms such as Facebook, WhatsApp, Twitter, and others, people share digital images, videos, and audio in large quantities. Especially in a crime scene investigation, digital evidence plays a crucial role in a courtroom. Manipulating video content with high-quality software tools is easier, which helps fabricate video content more efficiently. It is therefore necessary to develop an authenticating method for detecting and verifying manipulated videos. The objective of this pa

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Liba, Manopriya Jegaveerapandian, Jansi Rani Arockia, Periyaswamy Prakash, and Velusamy Sakthivel. "A survey on passive digital video forgery detection techniques." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6324–34. https://doi.org/10.11591/ijece.v13i6.pp6324-6334.

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Digital media devices such as smartphones, cameras, and notebooks are becoming increasingly popular. Through digital platforms such as Facebook, WhatsApp, Twitter, and others, people share digital images, videos, and audio in large quantities. Especially in a crime scene investigation, digital evidence plays a crucial role in a courtroom. Manipulating video content with high-quality software tools is easier, which helps fabricate video content more efficiently. It is therefore necessary to develop an authenticating method for detecting and verifying manipulated videos. The objective of th

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Prasad, G., G. Rakesh Reddy, M. Abhishek Guptha, B. Harinath, and N. Kamal Reddy. "Detection of Digital Image Forgery Using Deep-Learning." International Journal of Research Publication and Reviews 5, no. 5 (2024): 10032–37. http://dx.doi.org/10.55248/gengpi.5.0524.1376.

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Yamini, C. Tejaswani, M., M. Yamini, Y. Deepthi, and G. M. ANAND REDDY. "Enhancing Digital image forgery detection using transfer learning." International Journal of Research Publication and Reviews 6, no. 5 (2025): 11600–11602. https://doi.org/10.55248/gengpi.6.0525.18107.

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Deepika Dubey, Richa Rohatgi, and Seema R. Pathak. "Unveiling Digital Document Manipulation: A Case Study in Forensic Examination." Indian Journal of Forensic Medicine & Toxicology 18, no. 2 (2024): 46–52. http://dx.doi.org/10.37506/w909cd74.

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The ease of creating digital documents due to today’s technological advancements has led to a surge in white- collar crimes involving forgery and manipulation. Detecting these digital document alterations presents a unique set of challenges to forensic investigators. The present study aims to uncover a forgery in a digitally manipulated document and demonstrate the successful detection of digital document alterations. This study examines a real- life case to explore the methods utilized by forensic experts in detecting and evaluating document forgery. It highlights the crucial role of digital

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

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

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Abd, El-Latif Eman I., and Nour Eldeen Khalifa. "COVID-19 digital x-rays forgery classification model using deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1821–27. https://doi.org/10.11591/ijai.v12.i4.pp1821-1827.

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Nowadays, the internet has become a typical medium for sharing digital images through web applications or social media and there was a rise in concerns about digital image privacy. Image editing software’s have prepared it incredibly simple to make changes to an image's content without leaving any visible evidence for images in general and medical images in particular. In this paper, the COVID-19 digital x-rays forgery classification model utilizing deep learning will be introduced. The proposed system will be able to identify and classify image forgery (copy-move and splicing) manipulat

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Li, Li, Jianfeng Lu, Shanqing Zhang, Linda Mohaisen, and Mahmoud Emam. "Frame Duplication Forgery Detection in Surveillance Video Sequences Using Textural Features." Electronics 12, no. 22 (2023): 4597. http://dx.doi.org/10.3390/electronics12224597.

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Frame duplication forgery is the most common inter-frame video forgery type to alter the contents of digital video sequences. It can be used for removing or duplicating some events within the same video sequences. Most of the existing frame duplication forgery detection methods fail to detect highly similar frames in the surveillance videos. In this paper, we propose a frame duplication forgery detection method based on textural feature analysis of video frames for digital video sequences. Firstly, we compute the single-level 2-D wavelet decomposition for each frame in the forged video sequenc

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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 pre

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

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

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Abbadi, Nidhal, and Alyaa Mohsin. "Blind Digital Images Tampering Detection Based on Singular Value Decomposition." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 338–48. http://dx.doi.org/10.22266/ijies2020.1231.30.

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The growing use of digital images in a wide range of applications, and growing the availability of many editing photo software, cause to emerge a challenge to discover the images tampering. In this paper, we proposed a method to detect the most important type of forgery image (copy and move). We suggested many steps to classify the image as forgery or non-forgery image, started with preprocessing (included, convert image to gray image, de-noising, and image resize). Then, the image will be divided into several overlapping blocks. For each block, feature extracted (used it as a matching feature

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Adamski, T., and W. Nowakowski. "Security of Nyberg-Rueppel digital signatures without message recovery." Bulletin of the Polish Academy of Sciences Technical Sciences 62, no. 4 (2014): 817–25. http://dx.doi.org/10.2478/bpasts-2014-0090.

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Abstract The paper deals with Nyberg-Rueppel digital signatures without message recovery. Probability of signature forgery is analyzed and assessed. Some simple methods to minimize probability of signature forgery are proposed.

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Asif Tisekar, Abbas. "“Image Forgery Detection Using Transfer Learning”." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46928.

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Abstract: In today’s digital world, images are often shared and used as evidence in news, legal cases, and social media. However, it has become easier to manipulate images using editing tools, which can lead to false information and serious consequences. Detecting these changes, known as image forgery, is important to make sure images are trustworthy. Traditional methods for detecting image tampering often struggle with accuracy, especially when the edits are small or done carefully. These methods also require a lot of manual work and may not keep up with the fast-growing technology of image e

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Bhavani, A. Durga, S. Durga Shankar, Ch Aditya, and D. Vijaya Vani. "A Deep Learning-Based Approach for Image Forgery Detection and Classification Using YOLO and CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43511.

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and AI-assisted image manipulation tools, which The emergence of advanced image manipulation techniques in the digital age has made it extremely difficult to detect and categorize forged images. In order to identify and classify image forgeries, this study proposes a deep learning-based method that combines a Convolutional Neural Network (CNN) classifier with the You Only Look Once (YOLO) object detection model. The suggested system uses sophisticated feature extraction techniques to categorize images into authentic, copy-move, spliced, and deepfake forgeries after being trained on a custom da

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Djamshedovich, Sadullaev Jaxongir. "Analysis of objective and subjective elements of the crime of document forgery, selling, or using forged documents." American Journal of Political Science Law and Criminology 7, no. 3 (2025): 65–70. https://doi.org/10.37547/tajpslc/volume07issue03-11.

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Document forgery represents one of the most pervasive and multifaceted crimes across jurisdictions, affecting areas such as contract law, property rights, financial transactions, and public trust in government-issued records. This study analyzes the objective (actus reus) and subjective (mens rea) elements of document forgery, selling of forged documents, and using forged documents, drawing on an extensive body of international legal scholarship, case law, and statutory frameworks. We discuss the conceptual foundations of forgery, the delineation between material and intellectual falsification

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Djamshedovich, Sadullaev Jaxongir. "Analysis of objective and subjective elements of the crime of document forgery, selling, or using forged documents." American Journal of Political Science Law and Criminology 7, no. 4 (2025): 23–28. https://doi.org/10.37547/tajpslc/volume07issue04-05.

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Document forgery represents one of the most pervasive and multifaceted crimes across jurisdictions, affecting areas such as contract law, property rights, financial transactions, and public trust in government-issued records. This study analyzes the objective (actus reus) and subjective (mens rea) elements of document forgery, selling of forged documents, and using forged documents, drawing on an extensive body of international legal scholarship, case law, and statutory frameworks. We discuss the conceptual foundations of forgery, the delineation between material and intellectual falsification

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

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

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Mishra, Parul, Nishchol Mishra, Sanjeev Sharma, and Ravindra Patel. "Region Duplication Forgery Detection Technique Based on SURF and HAC." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/267691.

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Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, a

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S, MURALI, ANAMI BS, and CHITTAPUR GB. "DIGITAL PHOTO IMAGE- FORGERY DETECTION TECHNIQUES." International Journal of Machine Intelligence 4, no. 1 (2012): 405. http://dx.doi.org/10.9735/0975-2927.4.1.405-405.

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V., Kalpana,, Jayalakshmi, M., and Kishore, V.V. "Medical Image Forgery Detection By A Novel Segmentation Method With KPCA." CARDIOMETRY, no. 24 (November 30, 2022): 1079–85. http://dx.doi.org/10.18137/cardiometry.2022.24.10791085.

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One of the most challenging tasks of recent days is detecting digital image forgery. Effective image processing tools are developed with excellent technology enhancement, making an easy and comfortable process for image forgery. Because of these systems’ misusage, the verification of picture is too difficult. Therefore, image forgery detection uses different techniques according to the requirements of detection, efficiency, and type of forgery. This study proposes an efficient novel segmentation method with Kernel Principal Component Analysis (KPCA) for the detection of image forgery. The Norm

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Abd El-Latif, Eman I., and Nour Eldeen Khalifa. "COVID-19 digital x-rays forgery classification model using deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1821. http://dx.doi.org/10.11591/ijai.v12.i4.pp1821-1827.

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<p>Nowadays, the internet has become a typical medium for sharing digital<br />images through web applications or social media and there was a rise in<br />concerns about digital image privacy. Image editing software’s have prepared<br />it incredibly simple to make changes to an image's content without leaving<br />any visible evidence for images in general and medical images in particular.<br />In this paper, the COVID-19 digital x-rays forgery classification model<br />utilizing deep learning will be introduced. The proposed system will be able<b

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