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Journal articles on the topic 'Electronic forgery'
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1
Sezonov, Viktor, Mykhailo Fialka, Eduard Poltavski, Nataliia Prokopenko, and Maryna Fomenko. "Forensic Examination of Electronic Documents." Law, State and Telecommunications Review 14, no. 2 (2022): 81–93. http://dx.doi.org/10.26512/lstr.v14i2.40965.
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[Purpose] The purpose of the study is to reveal the concept and essence of forensic prevention of crimes of forgery of electronic documents, to identify problems in the use of information to establish a system of countering crime and document management. [Methodology] The following approaches were used in the work: system-structural, dialectical, empirical. Forgery of electronic documents and their use is investigated not only within the framework of a single criminal case but also by a set of crimes committed depending on the mechanism that is the main one in the structure of criminal technologies. [Findings] Lack of skills and knowledge about the latest forms of documents, methods of their forgery and use in the field of forensic investigations determine the reasons for the development of this condition. The analysis of investigative and judicial practice shows that cases of forgery of electronic documents are moved to separate proceedings due to the inability to fix the person who committed the crime. In some cases, court procedures are returned for additional investigation, since investigators cannot establish mechanisms for falsification tools and bring appropriate charges. [Practical Implications] The practical significance lies in the formation of proposals for improving or making changes to the legislation, effectively improving the activities of law enforcement agencies involved in countering or combating the forgery of documents.
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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, the significance of intent to deceive, and the punishments enforced. The article also explores emerging forms of forgery in electronic domains (e.g., digital signatures, manipulated images, and cyber-facilitated document falsification) and explains the forensic methodologies used to detect and prosecute these offenses. Ultimately, we highlight that while the objective elements require a demonstrable alteration or creation of a false document with legal significance, the subjective elements demand specific intent or knowledge of falsity aimed at deceiving a targeted party. The implications for legislative policy, prosecutorial practice, and emerging technologies in crime detection are discussed at length, drawing upon dozens of referenced scholarly works and statutory provisions.
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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, the significance of intent to deceive, and the punishments enforced. The article also explores emerging forms of forgery in electronic domains (e.g., digital signatures, manipulated images, and cyber-facilitated document falsification) and explains the forensic methodologies used to detect and prosecute these offenses. Ultimately, we highlight that while the objective elements require a demonstrable alteration or creation of a false document with legal significance, the subjective elements demand specific intent or knowledge of falsity aimed at deceiving a targeted party. The implications for legislative policy, prosecutorial practice, and emerging technologies in crime detection are discussed at length, drawing upon dozens of referenced scholarly works and statutory provisions.
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Chervinska, Lyubov Volodymyrivna. "On the Problem of Normative Definition of the Concept of «Official Document» in the Criminal Code of Ukraine." Alʹmanah prava, no. 15 (September 1, 2024): 654–59. https://doi.org/10.33663/2524-017x-2024-15-654-659.
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der Articles 357 «Forgery of Documents, Seals, Stamps and Forms, Sale or Use of Forged Documents, Seals, Stamps», 358 «Forgery of Documents, Seals, Stamps and Forms, Sale or Use of Forged Documents, Seals, Stamps», 366 «Official Forgery» of the Criminal Code of Ukraine. The author analyzes the legal regulation of circulation of official documents of Ukraine and establishes the legislator’s approach to determining the content of a document, an official document, and its subtypes — paper and electronic official documents. The study of the regulatory consolidation and doctrinal development of the features of an official document demonstrates that there are differences in the regulatory consolidation of the features of an official document in various legal acts, including the Criminal Code of Ukraine. At the same time, the «set» of features of the concept of «official document» in the Criminal Code of Ukraine is the most complete compared to the features of this concept in other legal acts. It is proposed to distinguish two groups of features of an official document as the subject matter of criminal offenses under Articles 357, 358, 366 of the Criminal Code of Ukraine: officialdom and content. The official nature of the document implies, first of all, the existence of the subject of its issuance, which must be authorized to draw up and/ or issue or certify an official document in accordance with the procedure provided for by the legislation of Ukraine, as well as the existence of a legally prescribed form of the document (paper or electronic), which also includes mandatory details of the document specified by regulatory legal acts. The content of an official document implies that the document must contain information recorded on any material carriers that confirms or certifies certain events, phenomena or facts that have caused or may cause legal consequences or can be used as evidence documents in law enforcement activities. Key words: document, official document, forgery, forgery in office, criminal offense, subject matter of a criminal offense, criminal liability, official document, document content, legal consequences, electronic document.
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Lin, Yih-Kai, and Ting-Yu Yen. "A Meta-Learning Approach for Few-Shot Face Forgery Segmentation and Classification." Sensors 23, no. 7 (2023): 3647. http://dx.doi.org/10.3390/s23073647.
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The technology for detecting forged images is good at detecting known forgery methods. It trains neural networks using many original and corresponding forged images created with known methods. However, when encountering unseen forgery methods, the technology performs poorly. Recently, one suggested approach to tackle this problem is to use a hand-crafted generator of forged images to create a range of fake images, which can then be used to train the neural network. However, the aforementioned method has limited detection performance when encountering unseen forging techniques that the hand-craft generator has not accounted for. To overcome the limitations of existing methods, in this paper, we adopt a meta-learning approach to develop a highly adaptive detector for identifying new forging techniques. The proposed method trains a forged image detector using meta-learning techniques, making it possible to fine-tune the detector with only a few new forged samples. The proposed method inputs a small number of the forged images to the detector and enables the detector to adjust its weights based on the statistical features of the input forged images, allowing the detection of forged images with similar characteristics. The proposed method achieves significant improvement in detecting forgery methods, with IoU improvements ranging from 35.4% to 127.2% and AUC improvements ranging from 2.0% to 48.9%, depending on the forgery method. These results show that the proposed method significantly improves detection performance with only a small number of samples and demonstrates better performance compared to current state-of-the-art methods in most scenarios.
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Trček, Denis. "Minimising the risk of electronic document forgery." Computer Standards & Interfaces 19, no. 2 (1998): 161–67. http://dx.doi.org/10.1016/s0920-5489(98)00010-5.
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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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Zulki Zulkifli Noor. "Proof of the Legal Power of Electronic Certificates Against Criminal Acts of Forgery." Journal of Law, Politic and Humanities 4, no. 6 (2024): 2571–75. https://doi.org/10.38035/jlph.v4i6.846.
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his study aims to analyze the legal force of electronic certificates in relation to the crime of forgery. Electronic certificates as one of the digital legal instruments are recognized in Law Number 11 of 2008 concerning Electronic Information and Transactions (UU ITE), providing a strong legal basis for their recognition and validity in the evidence process. However, in its application, there are various challenges, especially related to data security and the risk of forgery. Through a normative legal approach, this study examines various laws and regulations as well as relevant case studies to evaluate how electronic certificates can be used as valid evidence in court. The results of the study show that although electronic certificates have legal force, there are still gaps in regulation and implementation that can be exploited by criminals to commit forgery. Therefore, increased regulation and supervision are needed to ensure the reliability of electronic certificates in the legal process.
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Xue, Ziyu, Xiuhua Jiang, Qingtong Liu, and Zhaoshan Wei. "Global–Local Facial Fusion Based GAN Generated Fake Face Detection." Sensors 23, no. 2 (2023): 616. http://dx.doi.org/10.3390/s23020616.
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Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What’s more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global–local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods.
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Kadam, Kalyani, Swati Ahirrao, and Ketan Kotecha. "AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 4489. http://dx.doi.org/10.11591/ijece.v11i5.pp4489-4501.
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Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
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فتيحة عمارة. "جريمة التزوير الإلكتروني = The Crime of Electronic Forgery". مجلة القانون و المجتمع 7, № 1 (2019): 166–89. http://dx.doi.org/10.12816/0054549.
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Al-Ameri, Mohammed Abdulbasit Ali, Bunyamin Ciylan, and Basim Mahmood. "Spectral Data Analysis for Forgery Detection in Official Documents: A Network-Based Approach." Electronics 11, no. 23 (2022): 4036. http://dx.doi.org/10.3390/electronics11234036.
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Despite the huge advances in digital communications in the last decade, physical documents are still the most common media for information transfer, especially in the official context. However, the readily available document processing devices and techniques (printers, scanners, etc.) facilitate the illegal manipulation or imitation of original documents by forgers. Therefore, verification of the authenticity and detection of forgery is of paramount importance to all agencies receiving printed documents. We suggest an unsupervised forgery detection framework that can distinguish whether a document is forged based on the spectroscopy of the document’s ink. The spectra of the tested documents inks (original and questioned) were obtained using laser-induced breakdown spectroscopy (LIBS) technology. Then, a correlation matrix of the spectra was calculated for both the original and questioned documents together, which were then transformed into an adjacency matrix aiming at converting it into a weighted network under the concept of graph theory. Clustering algorithms were then applied to the network to determine the number of clusters. The proposed approach was tested under a variety of scenarios and different types of printers (e.g., inkjet, laser, and photocopiers) as well as different kinds of papers. The findings show that the proposed approach provided a high rate of accuracy in identifying forged documents and a high detection speed. It also provides a visual output that is easily interpretable to the non-expert, which provides great flexibility for real-world application.
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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 sequences. Secondly, textural features of each frame are extracted using the Gray Level of the Co-Occurrence Matrix (GLCM). Four second-order statistical descriptors, Contrast, Correlation, Energy, and Homogeneity, are computed for the extracted textural features of GLCM. Furthermore, we calculate four statistical features from each frame (standard deviation, entropy, Root-Mean-Square RMS, and variance). Finally, the combination of GLCM’s parameters and the other statistical features are then used to detect and localize the duplicated frames in the video sequences using the correlation between features. Experimental results demonstrate that the proposed approach outperforms other state-of-the-art (SOTA) methods in terms of Precision, Recall, and F1Score rates. Furthermore, the use of statistical features combined with GLCM features improves the performance of frame duplication forgery detection.
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Witasari, Aryani, and Aris Setiono. "PERLINDUNGAN HUKUM PENGGUNA JASA ELECTRONIC BANKING (E-BANKING) DI TINJAU DARI PERSPEKTIF HUKUM PIDANA DI INDONESIA." Jurnal Pembaharuan Hukum 2, no. 1 (2016): 126. http://dx.doi.org/10.26532/jph.v2i1.1422.
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Crime e-banking frequent one ATM card forgery. The perpetrators made a complete fake ATM cards with a magnetic stripe that already contains data records of card fraud. In addition to falsify the card, the perpetrators also know the PIN number of the card is duplicated / forged. ATM card forgery or duplication can be done because the necessary equipment to do so can be easily obtained in the market. This study uses normative legal approach by researching library materials or secondary data only, which relates to the legal protection of e-banking customers in the perspective of criminal law, using the approach of legislation, conceptual and historical. The study says that the legal protectiongiven to customers when there is a loss in e-banking transactions are bank provides its customers the facility if the losses caused by the e-banking, the bank facilitates its customers by providing legal assistance in litigation and non-litigation. 2) The legal protection of the victims of the features of e-banking in the standpoint of criminal law, is shared by the two concepts, namely the protection of the law implicitly and explicitly, of the concept of legal protection that customers have the force of law if the victim of the implications that exist within an e -banking.
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Shelar, Yogita, Dr Prashant Sharma, and Dr Chandan Singh D. Rawat. "Image Forgery Detection Using Integrated Convolution-LSTM (2D) and Convolution (2D)." International Journal of Electrical and Electronics Research 11, no. 2 (2023): 631–38. http://dx.doi.org/10.37391/ijeer.110253.
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Digital forensics and computer vision must explore image forgery detection and their related technologies. Image fraud detection is expanding as sophisticated image editing software becomes more accessible. This makes changing photos easier than with the older methods. Convolution LSTM (1D) and Convolution LSTM (2D) + Convolution (2D) are popular deep learning models. We tested them using the public CASIA.2.0 image forgery database. ConvLSTM (2D) and its combination outperformed ConvLSTM (1D) in accuracy, precision, recall, and F1-score. We also provided a related work on image forgery detection models and methods. We also reviewed publicly available datasets used in picture forgery detection research, highlighting their merits and drawbacks. Our investigation revealed the state of picture fraud detection and the deep learning models that worked well. Our work greatly impacts fraudulent photo detection. First, it highlights how important deep learning models are for picture forgery detection. Second, ConvLSTM (2D) + Conv (2D) detect image forgeries better than ConvLSTM (1D). Finally, our dataset analysis and proposed integrated approach help research construct more effective and accurate picture forgery detection systems.
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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 paper is to provide a comprehensive review of the passive methods for detecting video forgeries. This survey has the primary goal of studying and analyzing the existing passive techniques for detecting video forgeries. First, an overview of the basic information needed to understand video forgery detection is presented. Later, it provides an in-depth understanding of the techniques used in the spatial, temporal, and spatio-temporal domain analysis of videos, datasets used, and their limitations are reviewed. In the following sections, standard benchmark video forgery datasets and the generalized architecture for passive video forgery detection techniques are discussed in more depth. Finally, identifying loopholes in existing surveys so detecting forged videos much more effectively in the future are discussed.
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Nampoothiri V, Parameswaran, and Dr N. Sugitha. "An Optimized Fuzzy C-Means with Deep Neural Network for Image Copy-Move Forgery Detection." International Journal of Electrical and Electronics Research 12, no. 1 (2024): 308–14. http://dx.doi.org/10.37391/ijeer.120142.
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Copy Move Forgery Detection (CMFD) is one of the significant forgery attacks in which a region of the same image is copied and pasted to develop a forged image. Initially, the input digital images are preprocessed. Here the contrast of input image is enhanced. After preprocessing, Optimized Fuzzy C-means (OFCM) clustering is used to group the images into several clusters. Here the traditional FCM centroid selection is optimized by means of Salp Swarm Algorithm (SSA). The main inspiration of SSA is the swarming behavior of salps when navigating and foraging in oceans. Based on that algorithm, optimal centroid is selected for grouping images. Next, the unique features are extracted from each cluster. Due to the robust performance, the existing approach uses the SIFT-based framework for detecting CMFD. However, for some CMFD images, these approaches cannot produce satisfactory detection results. In order to solve this problem, the current method utilizes the stationary wavelet transform (SWT). After extracting the features, the CMFD detection is done by RB (Radial Basis) based neural network. Additionally, it is computed by means of diverse presentation metrics like sensitivity, specificity, accuracy; Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), False Negative Rate (FNR) and False Discovery Rate (FDR). The proposed copy move forgery detection method is implemented in the working platform of MATLAB.
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Murray, L. "Art attack: spot the forgery." Engineering & Technology 16, no. 8 (2021): 58–63. http://dx.doi.org/10.1049/et.2021.0806.
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Chandrakala, Chandrakala, and Mungamuri Sasikala. "An efficient novel dual deep network architecture for video forgery detection." International Journal of Reconfigurable and Embedded Systems (IJRES) 13, no. 2 (2024): 458. http://dx.doi.org/10.11591/ijres.v13.i2.pp458-471.
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The technique of video copy-move forgery (CMF) is commonly employed in various industries; digital videography is regularly used as the foundation for vital graphic evidence that may be modified using the aforementioned method. Recently in the past few decades, forgery in digital images is detected via machine intellect. The second issue includes continuous allocation of parallel frames having relevant backgrounds erroneously results in false implications, detected as CMF regions third include as the CMF is divided into inter-frame or intra-frame forgeries to detect video copy is not possible by most of the existing methods. Thus, this research presents the dual deep network (DDN) for efficient and effective video copy-move forgery detection (VCMFD); DDN comprises two networks; the first detection network (DetNet1) extracts the general deep features and second detection network (DetNet2) extracts the custom deep features; both the network are interconnected as the output of DetNet1 is given to DetNet2. Furthermore, a novel algorithm is introduced for forged frame detection and optimization of the falsely detected frame. DDN is evaluated considering the two benchmark datasets REWIND and video tampering dataset (VTD) considering different metrics; furthermore, evaluation is carried through comparing the recent existing model. DDN outperforms the existing model in terms of various metrics.
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Lin, Yih-Kai, and Hao-Lun Sun. "Few-Shot Training GAN for Face Forgery Classification and Segmentation Based on the Fine-Tune Approach." Electronics 12, no. 6 (2023): 1417. http://dx.doi.org/10.3390/electronics12061417.
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There are many techniques for faking videos that can alter the face in a video to look like another person. This type of fake video has caused a number of information security crises. Many deep learning-based detection methods have been developed for these forgery methods. These detection methods require a large amount of training data and thus cannot develop detectors quickly when new forgery methods emerge. In addition, traditional forgery detection refers to a classifier that outputs real or fake versions of the input images. If the detector can output a prediction of the fake area, i.e., a segmentation version of forgery detection, it will be a great help for forensic work. Thus, in this paper, we propose a GAN-based deep learning approach that allows detection of forged regions using a smaller number of training samples. The generator part of the proposed architecture is used to synthesize predicted segmentation which indicates the fakeness of each pixel. To solve the classification problem, a threshold on the percentage of fake pixels is used to decide whether the input image is fake. For detecting fake videos, frames of the video are extracted and it is detected whether they are fake. If the percentage of fake frames is higher than a given threshold, the video is classified as fake. Compared with other papers, the experimental results show that our method has better classification and segmentation.
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Restutomo Herjiwandono, Bayu, Sukisno Sukisno, and Nia Komalasari. "Perancangan Aplikasi Signature Electronic Dengan Qr-Code Pada Pt. Sankyu Indonesia Internasional." Jurnal Ilmiah Fakultas Teknik 4, no. 2 (2025): 149–89. https://doi.org/10.33592/jimtek.v4i2.7168.
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In the digital era, information technology is transforming various aspects of life, including education and business. Electronic Signature (eSign) have become a significant innovation in document verification and authentication, offering time efficiency and reduced administrative costs. PT. Sankyu Indonesia Internasional faces challenges in document management with manual Signature s, such as loss, damage, and forgery. To address this, an eSign application integrated with QR-Code was designed. This solution speeds up verification, reduces the risk of forgery, and enhances document Security. QR-Code assists in quick identification and information access. Combining eSign with QR-Code in a web application simplifies administration and document management at PT. Sankyu Indonesia Internasional, improving efficiency and Security, while protecting document integrity. This research aims to develop an eSign application with QR-Code specifically for PT. Sankyu Indonesia Internasional, enhancing efficiency, Security, and ease of information access in the company’s document management, and supporting the application of information technology within the company. Keywords: administration, e-sign, QR-Code, PT Sankyu
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Maysaa, Abd Ulkareem Naser, Talib Jasi Eman, and M. Al-Mashhadi Haider. "QR code based two-factor authentication to verify paper-based documents." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 1834–42. https://doi.org/10.12928/TELKOMNIKA.v18i4.14339.
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Important paper-based documents exposed to forgery such as: official certificates, birth, marriage, death certificates, selling and buying documents and other legal documents is more and more serious and sophisticated. With the purposes of fraud, appropriation of property, job application and assignment in order to swindle public authorities, this forgery has led to material loss, belief deterioration as well as social instability. There are many techniques has been proposed to overcome this issue such as: ink stamps, live signatures, documented the transaction in third party like the court or notary. In this paper, it’s proposed a feasible solution for forgery prevention for paper-based documents using cloud computing application. With the application of quick response bidirectional barcode and the usage of hash algorithm. The study aims at developing an electronic verification system for official and issued books (documents, endorsements, and other official books) to/from different sections of the Institute using QR technology.
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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<br />to identify and classify image forgery (copy-move and splicing) manipulation.<br />Alexnet, Resnet50, and Googlenet are used in this model for feature extraction<br />and classification, respectively. Images have been tampered with in three<br />classes (COVID-19, viral pneumonia, and normal). For the classification of<br />(Forgery or no forgery), the model achieves 0.9472 in testing accuracy. For<br />the classification of (Copy-move forgery, splicing forgery, and no forgery),<br />the model achieves 0.8066 in testing accuracy. Moreover, the model achieves<br />0.796 and 0.8382 for 6 classes and 9 classes problems respectively.<br />Performance indicators like Recall, Precision, and F1 Score supported the<br />achieved results and proved that the proposed system is efficient for detecting<br />the manipulation in images.</p><div align="center"> </div>
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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-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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Sun, Yi, Jun Zheng, Lingjuan Lyn, et al. "The Same Name Is Not Always the Same: Correlating and Tracing Forgery Methods across Various Deepfake Datasets." Electronics 12, no. 11 (2023): 2353. http://dx.doi.org/10.3390/electronics12112353.
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Deepfakes are becoming increasingly ubiquitous, particularly in facial manipulation. Numerous researchers and companies have released multiple datasets of face deepfakes labeled to indicate different methods of forgery. However, naming these labels is often arbitrary and inconsistent, leading to the fact that most researchers now choose to use only one of the datasets for research work. However, researchers must use these datasets in practical applications and conduct traceability research. In this study, we employ some models to extract forgery features from various deepfake datasets and utilize the K-means clustering method to identify datasets with similar feature values. We analyze the feature values using the Calinski Harabasz Index method. Our findings reveal that datasets with the same or similar labels in different deepfake datasets exhibit different forgery features. We proposed the KCE system to solve this problem, which combines multiple deepfake datasets according to feature similarity. We analyzed four groups of test datasets and found that the model trained based on KCE combined data faced unknown data types, and Calinski Harabasz scored 42.3% higher than combined by forged names. Furthermore, it is 2.5% higher than the model using all data, although the latter has more training data. It shows that this method improves the generalization ability of the model. This paper introduces a fresh perspective for effectively evaluating and utilizing diverse deepfake datasets and conducting deepfake traceability research.
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Gadekar,, Supriya S. "Roadmap for Digital Image Forgery Detection Using Deep Learning." Tuijin Jishu/Journal of Propulsion Technology 44, no. 5 (2023): 1732–48. http://dx.doi.org/10.52783/tjjpt.v44.i5.2849.
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Technology advances are prominent today while influencing all aspects of our lives. Misuse of information has also increased as a result of technical improvements. As a result, investigators have the enormous task of recognizing modified information and distinguishing this from genuine data. Among the most prevalent techniques for electronic image alteration is splicing, which includes replicating a specific section using the same or different photograph and transferring it to a new image. In the wake of this issue, picture identification of forgeries has arisen as a viable approach for confirming the validity of online photographs.
 Within this paper, we propose a strategy depending on the cutting-edge ResNet50v2 neural networks framework. This study describes an approach intended specifically for detecting splicing, among the most common forms of online picture forgeries. The VGG-16 convolutional neural network model is used in our technique. The recommended network topology accepts picture patchwork as data and generates identification outcomes on the patch, categorizing them as legitimate or fraudulent. We select pieces from the primary picture areas and the inserted splicing boundaries throughout the teaching stage. This paper proposes an effective technique for identifying splices in electronic photos, proving the usefulness of our deep learning-powered strategy and emphasizing its superior results over previous alternatives.
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Chen, L. F., Z. K. Liu, and B. Y. Peng. "Security protection against optical forgery attack." Optics & Laser Technology 168 (January 2024): 109889. http://dx.doi.org/10.1016/j.optlastec.2023.109889.
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Li, Qian, Rangding Wang, and Dawen Xu. "A Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise." Electronics 12, no. 6 (2023): 1362. http://dx.doi.org/10.3390/electronics12061362.
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Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, foreground objects of the frame are extracted, and then, reference SPN are trained using frames without foreground objects. Finally, the SPCE is calculated at the block level to distinguish forged objects from normal objects. Experimental results demonstrate that the method can accurately locate the tampered area and has higher detection accuracy. In terms of accuracy and F1-score, our method achieves 0.914 and 0.912, respectively.
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Abdulazeem Ahmed, Eman, Malek Alzaqebah, Sana Jawarneh, et al. "Comparison of specific segmentation methods used for copy move detection." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 2363. http://dx.doi.org/10.11591/ijece.v13i2.pp2363-2374.
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<p><span lang="EN-US">In this digital age, the widespread use of digital images and the availability of image editors have made the credibility of images controversial. To confirm the credibility of digital images many image forgery detection types are arises, copy-move forgery is consisting of transforming any image by duplicating a part of the image, to add or hide existing objects. Several methods have been proposed in the literature to detect copy-move forgery, these methods use the key point-based and block-based to find the duplicated areas. However, the key point-based and block-based have a drawback of the ability to handle the smooth region. In addition, image segmentation plays a vital role in changing the representation of the image in a meaningful form for analysis. Hence, we execute a comparison study for segmentation based on two clustering algorithms (i.e., k-means and super pixel segmentation with density-based spatial clustering of applications with noise (DBSCAN)), the paper compares methods in term of the accuracy of detecting the forgery regions of digital images. K-means shows better performance compared with DBSCAN and with other techniques in the literature.</span></p>
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Rai, Ashraf Fathi Al, and Nayel Musa AlOmran. "Criminal protection of electronic signatures from forgery in Jordanian and UAE legislation." International Journal of Electronic Governance 16, no. 2 (2024): 246–62. http://dx.doi.org/10.1504/ijeg.2024.140786.
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Alsakar, Yasmin M., Nagham E. Mekky, and Noha A. Hikal. "Detecting and Locating Passive Video Forgery Based on Low Computational Complexity Third-Order Tensor Representation." Journal of Imaging 7, no. 3 (2021): 47. http://dx.doi.org/10.3390/jimaging7030047.
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Great attention is paid to detecting video forgeries nowadays, especially with the widespread sharing of videos over social media and websites. Many video editing software programs are available and perform well in tampering with video contents or even creating fake videos. Forgery affects video integrity and authenticity and has serious implications. For example, digital videos for security and surveillance purposes are used as evidence in courts. In this paper, a newly developed passive video forgery scheme is introduced and discussed. The developed scheme is based on representing highly correlated video data with a low computational complexity third-order tensor tube-fiber mode. An arbitrary number of core tensors is selected to detect and locate two serious types of forgeries which are: insertion and deletion. These tensor data are orthogonally transformed to achieve more data reductions and to provide good features to trace forgery along the whole video. Experimental results and comparisons show the superiority of the proposed scheme with a precision value of up to 99% in detecting and locating both types of attacks for static as well as dynamic videos, quick-moving foreground items (single or multiple), zooming in and zooming out datasets which are rarely tested by previous works. Moreover, the proposed scheme offers a reduction in time and a linear computational complexity. Based on the used computer’s configurations, an average time of 35 s. is needed to detect and locate 40 forged frames out of 300 frames.
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Rajalakshmi, C., Al M. Germanus, and R. Balasubramanian. "Copy move forgery detection using key point localized super pixel based on texture features." Computer Optics 43, no. 2 (2019): 270–76. http://dx.doi.org/10.18287/2412-6179-2019-43-2-270-276.
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The most important barrier in the image forensic is to ensue a forgery detection method such can detect the copied region which sustains rotation, scaling reflection, compressing or all. Traditional SIFT method is not good enough to yield good result. Matching accuracy is not good. In order to improve the accuracy in copy move forgery detection, this paper suggests a forgery detection method especially for copy move attack using Key Point Localized Super Pixel (KLSP). The proposed approach harmonizes both Super Pixel Segmentation using Lazy Random Walk (LRW) and Scale Invariant Feature Transform (SIFT) based key point extraction. The experimental result indicates the proposed KLSP approach achieves better performance than the previous well known approaches.
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Zhao, Kaiqi, Xiaochen Yuan, Zhiyao Xie, Yan Xiang, Guoheng Huang, and Li Feng. "SPA-Net: A Deep Learning Approach Enhanced Using a Span-Partial Structure and Attention Mechanism for Image Copy-Move Forgery Detection." Sensors 23, no. 14 (2023): 6430. http://dx.doi.org/10.3390/s23146430.
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With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts 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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Kohli, Aditi, Abhinav Gupta, and Divya Singhal. "CNN based localisation of forged region in object-based forgery for HD videos." IET Image Processing 14, no. 5 (2020): 947–58. http://dx.doi.org/10.1049/iet-ipr.2019.0397.
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Moscatelli, Alberto. "Physically unclonable functions fight forgery." Nature Nanotechnology 17, no. 8 (2022): 818. http://dx.doi.org/10.1038/s41565-022-01194-1.
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Li, Zichen, Zhongxian Li, Yixian Yang, and Weilin Wu. "A new forgery attack on message recovery signatures." Journal of Electronics (China) 17, no. 3 (2000): 234–37. http://dx.doi.org/10.1007/s11767-000-0035-7.
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Huang, Shuan-Yu, Arvind Mukundan, Yu-Ming Tsao, Youngjo Kim, Fen-Chi Lin, and Hsiang-Chen Wang. "Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging." Sensors 22, no. 19 (2022): 7308. http://dx.doi.org/10.3390/s22197308.
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Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.
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Enumula, Mahesh, Dr M. Giri, and Dr V. K. Sharma. "A New Efficient Forgery Detection Method using Scaling, Binning, Noise Measuring Techniques and Artificial Intelligence (Ai)." International Journal of Innovative Technology and Exploring Engineering 12, no. 9 (2023): 17–21. http://dx.doi.org/10.35940/ijitee.i9703.0812923.
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In the market new updated editing tools and technologies are available to edit images and with help of these tools images are easily forged. In this research paper we proposed new forgery detection technique with estimation of noise on various scale of input image affect of noise in input image, frequency of images are also changed due to noise, noise signal correlated with original input images and in compressed images quantization level frequency also changed due to noise.We partition input image into M X N blocks, resized blocks are proceed further, image colors are also taken into consideration, each block noise value is evaluated at local level and global level. For each color channel of input image estimate local and global noise levels are estimated and compared using binning method. Also measured heat map of each block and each color channel of input image and all these values are stored in bins. Finally from all noise values calculate average mean value of noise, with these values decide whether input image is forgery or not, and performance of proposed method is compared with existing methods.
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Qazi, Tanzeela, Weiyao Lin, Samee U. Khan, et al. "Survey on blind image forgery detection." IET Image Processing 7, no. 7 (2013): 660–70. http://dx.doi.org/10.1049/iet-ipr.2012.0388.
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Taha Ahmed, Ismail, Baraa Tareq Hammad, and Norziana Jamil. "A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 1177. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp1177-1190.
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<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>
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Ms., Jaya Giri. "Examination of Tampered Electronic Documents 'Kamalvidya'." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 2221–31. https://doi.org/10.31142/ijtsrd18302.
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With the changing scenario of world, towards electronic passage it is important for one to maintain authenticity and move the document "As It Is" from the source to the destination in a defined manner. Computer reads any e document in binary language i.e. 0 and 1. The programme so designed is named "KamalVidya". Using this programme, it is possible to examine tampered electronic documents and will display the altered text in the allotted space. This programme can be used to investigate tampered e document as well as can be use to establish a secure passage for e document transfer. The programme so designed has in built function to detect and decipher obliteration, addition and deletion in e documents with capability to locate even a small "dot or space" added or deleted in the e document. It also includes comparison of two e documents and finding similarities and differences between them. Today computer forensics is not related to a single domain but it includes various thoughts in various fields which run the whole setup in a defined manner. Today forgery is not limited to civil criminal cases, it has wide hands in various areas such as banks, MNCs, legal proceedings and many more where electronic document plays major role. It is the need of time that an application should be made which will identify exact added deleted obliterated text in one go. Any type of electronic document can be tampered by various methods and this research applies to decipher and find out the original document from the forged or tempered one, as per the case. Ms. Jaya Giri "Examination of Tampered Electronic Documents 'Kamalvidya'" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18302.pdf
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Yang, Jiachen, Guipeng Lan, Shuai Xiao, Yang Li, Jiabao Wen, and Yong Zhu. "Enriching Facial Anti-Spoofing Datasets via an Effective Face Swapping Framework." Sensors 22, no. 13 (2022): 4697. http://dx.doi.org/10.3390/s22134697.
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In the era of rapid development of the Internet of things, deep learning, and communication technologies, social media has become an indispensable element. However, while enjoying the convenience brought by technological innovation, people are also facing the negative impact brought by them. Taking the users’ portraits of multimedia systems as examples, with the maturity of deep facial forgery technologies, personal portraits are facing malicious tampering and forgery, which pose a potential threat to personal privacy security and social impact. At present, the deep forgery detection methods are learning-based methods, which depend on the data to a certain extent. Enriching facial anti-spoofing datasets is an effective method to solve the above problem. Therefore, we propose an effective face swapping framework based on StyleGAN. We utilize the feature pyramid network to extract facial features and map them to the latent space of StyleGAN. In order to realize the transformation of identity, we explore the representation of identity information and propose an adaptive identity editing module. We design a simple and effective post-processing process to improve the authenticity of the images. Experiments show that our proposed method can effectively complete face swapping and provide high-quality data for deep forgery detection to ensure the security of multimedia systems.
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Kumar, B. L. V. Vinay, and K. Raja Kumar. "Blockchain Solution for Evidence Forgery Detection." Journal of Computational and Theoretical Nanoscience 17, no. 12 (2020): 5570–76. http://dx.doi.org/10.1166/jctn.2020.9454.
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Rapidly improving video editing software tools have made video content manipulation feasible. Consequently malicious attackers are trying to manipulate the videos. Detecting video tampering is a major need for many applications. In this paper we propose a model called Evidence chain based on Blockchain to ensure the credibility of the video. Unlike bitcoin which is a digital currency the Proposed system documents video hash by using hash based technology and elliptic curve cryptography. Video segments are hashed and stored in chronological order as a chain of blocks which are detectable and non-altering guaranteeing the validity of the video information. This research is significant in establishing the trust between any two parties.
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Jerold, Jerold, Suhaidi Suhaidi, and Isnaini Isnaini. "Upaya Imigrasi dalam Penerapan Sanksi Pidana Terhadap Pengguna Dokumen Perjalanan Palsu." ARBITER: Jurnal Ilmiah Magister Hukum 1, no. 2 (2019): 126–34. http://dx.doi.org/10.31289/arbiter.v1i2.115.
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The purpose of this study is to find out how the forms of falsification of the Travel Documents of the Republic of Indonesia, how the application of criminal sanctions for the forgeries of the Travel Documents of the Republic of Indonesia according to positive Indonesian law and how immigration actions in overcoming the fraudulent Travel Documents of the Republic of Indonesia occur. The research method used in this research is descriptive analysis, data collection techniques with literature studies and interviews, the types of data are primary data and secondary data, while the data analysis using descriptive cumulative data analysis is descriptive. From the results of research that cases of forgery of Travel Documents of the Republic of Indonesia (passports), can be classified into four forms of forgery of Travel Documents of the Republic of Indonesia (passports): original documents obtained illegally (using false or incorrect data), original documents that have been subjected to changes, documents that are completely falsified (duplication), original documents used by others (Impostor). Articles used in the crime of forgery of passports are article 119, article 126, article 127, article 129. Countermeasures to prevent the falsification of Travel Documents of the Republic of Indonesia, such as by: Issuance of Electronic Passport (e-passport), Photo and fingerprinting process finger recording the applicant's data, interview process when the applicant submits a passport application, Provision of Passport Safety Features.
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Sumaiya Shaikh, Et al. "Video Forgery Detection: A Comprehensive Study of Inter and Intra Frame Forgery With Comparison of State-Of-Art." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 1487–97. http://dx.doi.org/10.17762/ijritcc.v11i9.9130.
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Availability of sophisticated and low-cost smart phones, digital cameras, camcorders, surveillance CCTV cameras are extensively used to create videos in our daily life. The prevalence of video sharing techniques presently available in the market are: YouTube, Facebook, Instagram, snapchat and many more are in utilization to share the information related to videos. Besides this, there are many software which can edit the content of video: Window Movie Maker, Video Editor, Adobe Photoshop etc., with this available software anyone can edit the video content which is called as “Forgery” if edited content is harmful. Usually, videos play a vital role in terms of proof in crime scene. The Victim is judged by the proof submitted by the lawyer to the court. Many such cases have evidenced that the video being submitted as proof is been forged. Checking the authentication of the video is most important before submitting as proof. There has been a rapid development in deep learning techniques which have created deepfake videos where faces are replaced with other faces which strongly made a belief of saying “Seeing is no longer believing”. The available software which can morph the faces are FakeApp, FaceSwap etc., the increased technology really made the Authentication of proofs very doubtful and un-trusty which are not accepted as proof without proper validation of the video. The survey gives the methods that are capable of accurately computing the videos and analyses to detect different kinds of forgeries. It has revealed that most of the existing methods are relying on number of tampered frames. The proposed techniques are with compression, double compression codec videos where research is being carried out from 2016 to present. This paper gives the comprehensive study of techniques, algorithms and applications designed and developed to detect forgery in videos.
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Zhang, Tianyi, and Yong Wang. "RLFAT: A Transformer-Based Relay Link Forged Attack Detection Mechanism in SDN." Electronics 12, no. 10 (2023): 2247. http://dx.doi.org/10.3390/electronics12102247.
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SDN is a modern internet architecture that has transformed the traditional internet structure in recent years. By segregating the control and data planes of the network, SDN facilitates centralized management, scalability, dynamism, and programmability. However, this very feature makes SDN controllers vulnerable to cyber attacks, which can cause network-wide crashes, unlike conventional networks. One of the most stealthy attacks that SDN controllers face is the relay link forgery attack in topology deception attacks. Such an attack can result in erroneous overall views for SDN controllers, leading to network functionality breakdowns and even crashes. In this article, we introduce the Relay Link Forgery Attack detection model based on the Transformer deep learning model for the first time. The model (RLFAT) detects relay link forgery attacks by extracting features from network flows received by SDN controllers. A dataset of network flows received by SDN controllers from a large number of SDN networks with different topologies was collected. Finally, the Relay-based Link Forgery Attack detection model was trained on this dataset, and its performance was evaluated using accuracy, recall, F1 score, and AUC metrics. For better validation, comparative experiments were conducted with some common deep learning models. The experimental results show that our proposed model (RLFAT) has good performance in detecting RLFA and outperforms other models.
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Gardella, Marina, Pablo Musé, Jean-Michel Morel, and Miguel Colom. "Forgery Detection in Digital Images by Multi-Scale Noise Estimation." Journal of Imaging 7, no. 7 (2021): 119. http://dx.doi.org/10.3390/jimaging7070119.
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A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.
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Barglazan, Adrian-Alin, Remus Brad, and Constantin Constantinescu. "Image Inpainting Forgery Detection: A Review." Journal of Imaging 10, no. 2 (2024): 42. http://dx.doi.org/10.3390/jimaging10020042.
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In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods as well as those based on neural networks. Furthermore, we will present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all the above, the outcome of this study is to provide a comprehensive perspective on the abilities and constraints of detecting object removal via the inpainting procedure in images.
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Yu, Miaomiao, Jun Zhang, Shuohao Li, Jun Lei, Fenglei Wang, and Hao Zhou. "Deep forgery discriminator via image degradation analysis." IET Image Processing 15, no. 11 (2021): 2478–93. http://dx.doi.org/10.1049/ipr2.12234.
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Xu, De, and Qing Yang. "The Systems Approach and Design Path of Electronic Bidding Systems Based on Blockchain Technology." Electronics 11, no. 21 (2022): 3501. http://dx.doi.org/10.3390/electronics11213501.
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The electronic tendering and bidding system has realized the digitalization, networking, and high integration of the whole process of tendering, bidding, bid evaluation, and contract, which has a wide range of applications. However, the trust degree, cooperation, and transaction efficiency of the parties involved in electronic bidding are low, and bidding fraud and collusion are forbidden repeatedly. Blockchain technology has the characteristics of decentralization, transparent transactions, traceability, non-tampering and forgery detection, and data security. This paper proposes a design path of an electronic bidding system based on blockchain technology, which aims to solve the efficiency, trust, and security of the electronic trading process. By building the underlying architecture platform of blockchain and embedding the business process of electronic bidding, this realizes the transparency, openness, and traceability during the whole process of electronic bidding. This paper uses qualitative and quantitative methods to prove the effectiveness of the system.