Forgery Detection Techniques Research Articles

DWT and LBP hybrid feature based deep learning technique for image splicing forgery detection

DWT and LBP hybrid feature based deep learning technique for image splicing forgery detection

An image Analysis Algorithm for Detecting Modified Content on the Internet Against Cybercrime. A Technique for Estimating the Probability of Modification

Aim: The aim of this study is to present a cutting-edge image analysis algorithm designed to estimate the probability of modifications in digital images, a critical component in cybersecurity for detecting altered content on the Internet. This technique enhances the accuracy of change detection by distinguishing real inconsistencies from accidental changes. As the problem of sophisticated image processing software and fake images continues to rise on the Internet, the issue of image authenticity becomes increasingly crucial in many areas of science and society. Traditional visual inspection methods are inadequate, as research shows human perception is limited in recognizing the authenticity of images. This study aims to improve the accuracy of detecting modifications by distinguishing authentic inconsistencies from random anomalies Project and methods: This project involves developing an algorithm that leverages noise analysis and a statistical validation step to detect image modifications. The algorithm employs the False Positive Rate Index (FPRI) to manage false positives, providing a reliable confidence level for each detection. The method includes visual exploration to interpret detections and compares the algorithm’s performance with other state-of-the-art techniques in scenarios such as retouching, coloring, and merging. The algorithm analyzes noise in images, considering changes in its statistical properties due to image processing like noise reduction, demosaicing, chromatic aberration correction, color matching, gamma correction, and image compression. It compares global noise curves with local ones to identify potential changes, with results visualized using heat maps. Results: The algorithm demonstrates promising performance across various test scenarios, successfully identifying true modifications among random events. However, it shows limitations in detecting certain types of forgeries, such as internal copy transfer and merging in high-noise areas. Tests conducted on various data sets, including scenarios of retouching, coloring, and merging, confirm the algorithm’s effectiveness in detecting modifications. The results are compared with other noise analysis methods such as Splicebuster, Noiseprint, and Mahdian, showing superior performance in most cases. Conclusions: The proposed method represents a significant advance in image forgery detection within the field of cybersecurity, offering rigorous statistical validation and a measurable level of detection confidence. Despite some challenges in detecting specific types of manipulation, the algorithm is a valuable tool in digital image analysis and forensic research, enhancing the reliability of image alteration detection. As the use of digital technologies increases, so does the importance of image interpretation in areas like fire investigations and other incidents involving records from both classical and thermographic imaging systems. This method is particularly valuable in forensic investigations, where objective evidence of image authenticity is crucial. Future challenges include the use of neural networks to replicate the characteristic noise of original images to hide alterations, underscoring the need for continued development of advanced forgery detection techniques. Keywords: wykrywanie modyfikacji obrazu, cyberbezpieczeństwo, analiza obrazu, wykrywanie fałszywych obrazów

A copy-move forgery detection technique using DBSCAN-based keypoint similarity matching

A copy-move forgery detection technique using DBSCAN-based keypoint similarity matching

Advance Image Forgery Detection

Abstract :- In the digital era, the proliferation of sophisticated image editing tools has made image forgery a prevalent issue, posing significant challenges in various fields, including legal proceedings, journalism, and digital security. Advanced image forgery detection techniques are crucial to ensuring the authenticity and integrity of digital images. This paper explores the state-of-the-art methods and technologies employed in the detection of image forgeries. We focus on both traditional and deep learning-based approaches, highlighting their respective strengths and weaknesses. Traditional methods, such as pixel-based, format-based, and geometry-based techniques, rely on the intrinsic properties of images to identify inconsistencies and manipulations. These methods are effective in detecting simple forgeries but often fall short against more sophisticated alterations. On the other hand, deep learning-based approaches, leveraging convolutional neural networks (CNNs) and generative adversarial networks (GANs), have shown significant promise in identifying subtle and complex forgeries by learning high-level features from large datasets. In conclusion, while significant advancements have been made in image forgery detection, continuous research and development are necessary to stay ahead of emerging forgery techniques. The integration of multidisciplinary approaches and the collaboration between academia and industry will be pivotal in advancing the efficacy of forgery detection systems. Keywords : Image Forgery Detection, Deep Learning, Digital Image Authentication, Pixel-based Methods, Image Manipulation. Image Manipulation.

Securing Medical Imaging: A Comprehensive Review and Prospective Paths for Image Authentication

Abstract: As medical image authentication systems depend more and more on cutting-edge technologies, the integrity and dependability of diagnostic procedures are seriously threatened by adversarial attacks. This abstract reviews the latest advancements in Deep Learning (DL)-specific image forgery detection techniques, highlighting common splicing and copy-move attacks as well. This survey covers a wide range of methods used in medical image forgery detection and highlights the role that sophisticated models play in maintaining the accuracy of medical images. The study looked at a range of deep learning and machine learning models as well as methods for isolating photos and using Generative Adversarial Networks (GANs) to detect tampering. The results showed that it is possible to reliably recognize intentionally created anomalies in medical imaging. The case studies show that deep learning performs exceptionally well in correctly identifying scans with injected tumours, especially when it comes to the localization of the region of interest. This works well in situations where localization is not feasible, as reduced-negativespace scans show.

Copy-Move Video Forgery Detection Techniques: A Systematic Survey with Comparisons, Challenges and Future Directions

Copy-Move Video Forgery Detection Techniques: A Systematic Survey with Comparisons, Challenges and Future Directions

Passive Image Forgery Detection Techniques: A Review, Challenges, and Future Directions

Passive Image Forgery Detection Techniques: A Review, Challenges, and Future Directions

Squirrel Henry Gas Solubility Optimization driven Deep Maxout Network with multi‐texture feature descriptors for digital image forgery detection

SummaryThe ability to detect image forgeries is a crucial component in solving many problems, especially social ones that arise in legal proceedings, on Facebook, and so forth. Generally, forgery detection attempts to predict the artifacts through evaluating the differences in texture properties of an image. One division of an image is copied in a comparable image, typically at a different position, in a process known as copy move forgery. The two main types of copy move forgery prediction are keypoint‐based and block‐based techniques. While the block‐based approach takes more time, the keypoint‐based model performs poorly. The goal of this research was to develop a method for detecting image counterfeiting called the Squirrel Henry Gas Solubility Optimization algorithm‐based Deep Maxout Network (SHGSO‐based DMN). Here, Steerable Pyramid Transform (SPT) decomposition is done in order to obtain quantity of multi‐scale and multi‐oriented sub bands. For the purpose of detecting forgeries, the Deep Maxout Network which was constructed using the SHGSO model is deployed. With accuracy of 0.9507, True Negative Rate (TNR) of 0.9568, and True Positive Rate (TPR) of 0.9517, the new method outperformed other current forgery detection techniques.

Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction

Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction

Self-Information Forgery Mining for Face Forgery Detection

Abstract In the face of rapid advances in face forgery technology, effective detection methods have become crucial to maintain the authenticity of digital media. Deep learning technology has provided new strategies for recognizing and preventing face forgery in recent years. In this study, a new face forgery detection technique is proposed by utilizing self-information theory, which improves the accuracy and robustness of detection by mining forgery traces, especially in diverse forgery scenarios. The study extracts face features through an improved high-resolution network HRNet and optimizes identity information extraction by combining facial reenactment techniques to detect forged faces efficiently. Experiments have been conducted on several mainstream forged face datasets, and the method presented in this paper can effectively improve the detection performance with an average accuracy of 74.75% on C40 recompressed images. Comparison experiments show that this research method’s frame-level and video-level detection accuracy on the Celeb-DF dataset are 0.9846 and 0.9985, respectively, which are higher than those of existing techniques. Cross-library tests validate the method’s generalization performance, and the AUC metric remains at 0.7305 even in low-quality video environments, which shows good resistance to environmental interference. This study proposes a self-information forgery mining technique that enhances forgery detection accuracy while demonstrating superior generalization ability.

Wavelet Based Inpainting Detection

With the advancement in image editing tools, manipulating digital images has become alarmingly easy. Inpainting, which is used to remove objects or fill in parts of an image, serves as a powerful tool for both image restoration and forgery. This paper introduces a novel approach for detecting image inpainting forgeries by combining DT-CWT with Hierarchical Feature segmentation and with noise inconsistency analysis. The DT-CWT offers several advantages for this task, including inherent shift-invariance, which makes it robust to minor manipulations during the inpainting process, and directional selectivity, which helps capture subtle artifacts introduced by inpainting in specific frequency bands and orientations. By first applying colour image segmentation and then analysing for each segment, noise inconsistency obtained via DT-CW we can identify patterns indicative of inpainting forgeries. The proposed method is evaluated on a benchmark dataset created for this purpose and is compared with existing forgery detection techniques. Our approach demonstrates superior results compared with SOTA in detecting inpainted images. The proposed methodology source code is uploaded here: https://github.com/jmaba/Wavelet-based-inpainting-detection

A novel method for real-time object-based copy-move tampering localization in videos using fine-tuned YOLO V8

The research community faces challenges for video forgery detection techniques as advancements in multimedia technology have made it easy to alter the original video content and share it on electronic and social media with false propaganda. The copy-move attack is the most commonly practiced type of attack in videos/images, where an object is copied and moved into the current frame or any other frame of the video. Hence an illusion of recreation can be created to forge the content. It is very difficult to differentiate to uncover the forgery traces by the naked eye. Hence, a passive method-based algorithm is proposed to scientifically investigate the statistical properties of the video by normalizing the median difference of the frames at the pixel level, and graphical analysis successfully shows the clear peak in the forged region. After that, a new deep learning approach, “You Only Look at Once”, the latest eighth version of YOLO, is tuned and trained for the localization of forged objects in the real-time domain. The validation and testing results obtained from the trained YOLO V8 are successfully able to detect and localize the forged objects in the videos with mean average precision (mAP) of 0.99, recall is 0.99, precision is 0.99, and highest confidence score. The proposed YOLO V8 is fine-tuned in three different ways, and the performance of the proposed method outperforms existing state-of-the-art techniques in terms of inference speed, accuracy, precision, recall, testing, and training time.

Modeling of intelligent hyperparameter tuned deep learning based copy move image forgery detection technique

Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters.

A survey on passive digital video forgery detection techniques

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.

A Comprehensive Survey on Methods for Image Integrity

The outbreak of digital devices on the Internet, the exponential diffusion of data (images, video, audio, and text), along with their manipulation/generation also by Artificial Intelligence (AI) models, e.g., Generative Adversarial Networks (GANs), have created a great deal of concern in the field of forensics. A malicious use can affect relevant application domains, which often include counterfeiting biomedical images, and deceiving biometric authentication systems, as well as their use in scientific publications, in the political world, and even in school activities. It has been demonstrated that manipulated pictures most likely represent indications of malicious behavior, such as photos of minors to promote child prostitution or false political statements. Following this widespread behavior, various forensic techniques have been proposed in the scientific literature over time both to defeat these spoofing attacks as well as to guarantee the integrity of the information. Focusing on Image Forensics, which is currently a very hot topic area in Multimedia Forensics, this paper will discuss the whole scenario in which a target image could be modified. The aim of this comprehensive survey will be 1) to provide an overview of the types of attacks and contrasting techniques and 2) to evaluate to what extent the former can deceive prevention methods and the latter can identify counterfeit images. The results of this study highlight how forgery detection techniques, sometimes limited to a single type of real scenario, are not able to provide exhaustive countermeasures and could/should therefore be combined. Currently, the use of neural networks, such as CNNs, is already heading, synergistically, in this direction.

Gabor Filter and Centre Symmetric-Local Binary Pattern based technique for forgery detection in images

Gabor Filter and Centre Symmetric-Local Binary Pattern based technique for forgery detection in images

Enhanced Adaptive Over-Segmentation Technique for Image Forgery Detection under Noise Attacks

The problem of picture fraud has become more pervasive in the current digital era as a direct result of the ease with which complex image manipulation programs can be accessed. This research study provides a comprehensive analysis of several methodologies and approaches that are used to detect and localize cases of picture fraud. The primary objective is to provide a clear understanding of the most cutting-edge methods that are now being utilized, as well as the problems and opportunities that lie ahead. In the field of picture forensics, the focus of this article is on several distinct types of image fraud, such as copy-move, splicing, retouching, and inpainting forgeries. We study the underlying principles that lie behind frequently used detection methods, such as block matching, feature-based analysis, and approaches that are anchored in deep learning. In addition, we go over the benefits and drawbacks of each strategy, focusing on how they apply to a variety of settings. In addition, this article takes a look at the datasets that are typically utilized for the training and evaluation of forgery detection algorithms, highlighting both the benefits and the limitations of those datasets. In addition to this, we examine the numerous assessment metrics that are utilized in order to evaluate the performance of the various methods, with a particular focus on the requirement for standardized benchmark datasets and evaluation methodologies. In addition, we discuss the obstacles that are presented when attempting to identify picture fraud in the real world. These issues include the need to deal with photos that have been compressed, images that have varying resolutions, and the existence of post-processing effects. In this article, we will discuss the significance of multi-modal analysis and the fusion of information obtained from a variety of sources in order to improve the reliability of counterfeit detection systems. The approaches that are used to detect picture counterfeiting are the topic of the next section of this review. We look at several techniques, such as those based on segmentation, texture analysis, and deep neural networks, in order to determine the precise position of forged sections inside a picture. We go through their precision, the amount of computing complexity they have, and the possible uses they have. In conclusion, we discuss the potential of the future. In order to identify and localize forged images, it is necessary to keep up with the latest innovations in image editing software and build forgery detection systems that are more technologically advanced and effective. This is because picture editing techniques are always being refined and improved. We offer prospective research avenues, such as Explainable AI, Generative Adversarial Networks (GANs) for forgeries production, and hybrid techniques to combine the strengths of various detection methods. Specifically, we focus on hybrid approaches to combine the strengths of diverse detection methods. In conclusion, the purpose of this study is to offer academics and industry professionals in This article provides a comprehensive review of the present state of identifying and localizing picture forgeries, which falls under the umbrella of image forensics as a discipline. It encompasses the most recent developments in forgery detection and localisation, making it possible to gain an in-depth comprehension of the subject matter. It is possible to pave the way for more effective and reliable forgery detection systems to protect the integrity of digital pictures in a variety of applications if we grasp the existing approaches as well as the obstacles that they provide.

Enhanced Techniques for Detecting Copy-Move Forgery Using Machine Learning Algorithms

One of the most important tasks in digital forensics to find instances of modified content is the detection of copy-move forgery (CMF) in videos. Copy-move forgery includes taking a section of a video, pasting it into another movie, and then hiding or changing that section. As a consequence of advancements in network technology, low-cost multimedia devices, intelligent image or video editing software, and broad adoption of digital multimedia coding standards, the number of applications for digital multimedia has significantly risen in recent years. Establishing if a video is legitimate or not is one of the trickiest areas of video forensics. This may be a crucial responsibility when recordings are used as primary evidence to influence decisions, such as in a court of law. Therefore, we provide a novel machine learning-based copy-move forgery detection technique in this research. Weiner filter is first used to gather and pre- process video data. The pre-processed video data are then segmented using a threshold-based technique to image segmentation. Finally, we suggest a novel integrated stochastic random neighbouring approach (ISRNA) for categorizing videos. Our suggested technique is compared and contrasted with traditional ways to demonstrate the efficacy of the suggested method. Results from experiments show that our suggested strategy performs better than traditional ways.

Identification of Image Forgeries using Machine Learning - A Review

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 forgery detection techniques has become more important than ever. They are required to protect the authenticity of images and avoid the spread of fabricated and fake news. In this study the overview of the existing methods for identifying forgeries in images, and the summary of the issues found in these methods are discussed.

A New Efficient Forgery Detection Method using Scaling, Binning, Noise Measuring Techniques and Artificial Intelligence (Ai)

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.