Digital Image Forgery Research Articles

Detection of counterfeit images using SIFT and PNN

Manipulation of digital images presents a significant challenge, primarily due to the continuous advancement and sophistication of image enhancement tools. As a consequence, image feature extraction and matching have become pivotal areas of research in the field of image processing. Among the various techniques developed, the Scale-Invariant Feature Transform algorithm has gained widespread recognition for its robustness and superior performance in feature matching tasks when compared to other existing methods. In this context, our proposed method, which integrates the Scale-Invariant Feature Transform algorithm with a probabilistic neural network, offers a novel approach to digital image classification specifically aimed at counterfeit image detection. This integrated system not only enhances the accuracy of feature extraction but also leverages the probabilistic neural network to improve classification efficacy. Empirical results indicate that this hybrid approach achieves a remarkable relative error rate of just 0.666%, underscoring its potential as a reliable tool for combating digital image forgery. Such advancements are crucial for maintaining the integrity of digital media in various applications, from forensic analysis to digital content authentication.

An efficient novel dual deep network architecture for video forgery detection

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.

이미지 특징 강화와 앙상블 학습을 활용한 디지털 조작 이미지 검출 프레임워크

이미지 특징 강화와 앙상블 학습을 활용한 디지털 조작 이미지 검출 프레임워크

Image splicing detection using low-dimensional feature vector of texture features and Haralick features based on Gray Level Co-occurrence Matrix

Digital image forgery has become hugely widespread, as numerous easy-to-use, low-cost image manipulation tools have become widely available to the common masses. Such forged images can be used with various malicious intentions, such as to harm the social reputation of renowned personalities, to perform identity fraud resulting in financial disasters, and many more illegitimate activities. Image splicing is a form of image forgery where an adversary intelligently combines portions from multiple source images to generate a natural-looking artificial image. Detection of image splicing attacks poses an open challenge in the forensic domain, and in recent literature, several significant research findings on image splicing detection have been described. However, the number of features documented in such works is significantly huge. Our aim in this work is to address the issue of feature set optimization while modeling image splicing detection as a classification problem and preserving the forgery detection efficiency reported in the state-of-the-art. This paper proposes an image-splicing detection scheme based on textural features and Haralick features computed from the input image’s Gray Level Co-occurrence Matrix (GLCM) and also localizes the spliced regions in a detected spliced image. We have explored the well-known Columbia Image Splicing Detection Evaluation Dataset and the DSO-1 dataset, which is more challenging because of its constituent post-processed color images. Experimental results prove that our proposed model obtains 95% accuracy in image splicing detection with an AUC score of 0.99, with an optimized feature set of dimensionality of 15 only.

Image Forgery Detection Using Machine Learning

Digital Image Forgery can be done by deceiving the digital image to mask some meaningful or important data of the image. It is usually difficult to spot out the manipulated region of the original image. To sustain the uprightness and legitimacy of the image, the detection of forgery in the image is mandating. Acclimation of the modern way of life and advancement in photography gadgetry has made exploitation of digital image easy with the help of image editing software. Therefore, it is crucial to detect such image forgery operations in the images. The image forgery detection can be done based on object removal, object addition, unusual size modifications in the image. Images are one of the powerful media for communication. In this project we have used algorithms such as Copy move, Canny Edge Defection, Structure Similarity index, Hierarchical Agglomerate Clustering and Neural Network algorithms with improved accuracy rate.

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.

Detecting Digital Image Forgeries with Copy-Move and Splicing Image Analysis using Deep Learning Techniques

Detecting Digital Image Forgeries with Copy-Move and Splicing Image Analysis using Deep Learning Techniques

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of copy-move tampering identification have been discussed along with benchmarking study. In this study, three combination of methods like discrete cosine transform with Fast K-Means, discrete wavelet transform with Fast K-Means and discrete cosine transform with Fuzzy-C means can efficiently locate duplicated regions with quantitative parameter i.e. 92%, 83% and 92% respectively. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed.

Radiograph Manufacturer and Model Identification Using Deep-RSI

Malware attacks of healthcare institutions are simultaneously becoming more common and more sophisticated. Artificial intelligence (AI) has resulted in the ability to rapidly alter or generate false images, advancing the ease of forgery of digital images. Digital image manipulation and substitution of radiographs are major threats to healthcare institutions because these altered images may affect patient care. Identifying the source (manufacturer, model) of radiology images is one method of validating the origin of radiology images in a healthcare system. In a previous study, researchers demonstrated that features from magnetic resonance imaging (MRI) could be used to trace and authenticate the source of the MRI images. We previously developed and tested the Deep learning for Radiograph Source Identification (Deep-RSI) approach for source identification of radiographs obtained of the upper extremities (hands, wrists, forearms, elbows, and shoulders). In this research, we present an empirical and quantitative investigation using deep learning to validate the source of digital radiographic images of the lower extremities (knees, legs, ankles, and feet). A convolutional neural network (CNN) is employed to extract features, which are then followed by three fully connected layers (FCNN). To ensure that our proposed method is a content-free approach, we added a new layer before the CNN to extract the initial content-free pixels and train the features using the CNN and FCNN layers. This proposed approach was used to identify the source of each digital image of a lower extremity. Adult patients of both sexes who had radiographs of the lower extremities at Mayo Clinic between 01/01/2010 and 12/31/2021 were evaluated. The data was randomly split by patient into training/validation and test datasets. There were 9 radiographic machine models and 6 manufacturers. Deep-RSI had an accuracy of 99.00% (AUC= 0.99) and 97.00% (AUC=0.94) for detecting the manufacturer and model of the radiographic machine for radiographs of the feet respectively, confirming that forensic evaluation of radiographs can be performed. This is the first medical forensics examination of this type to identify and confirm the source origins for radiographs of the lower extremities. This technique may be helpful to detect radiology malware attacks and scientific fraud.
 

Performance Evaluation of Local Binary Patterns LBP for Copy-Move Forgery Detection in Digital Images: A Comparative Study

Copy-move forgery is a type of image tampering that involves copying a portion of an image and pasting it to another part of the same image with the intention of deceiving the viewer. In recent years, many approaches have been proposed to detect copy-move forgery, including those based on local binary patterns (LBP). In this paper, we perform a comprehensive evaluation of LBP-based methods for copy-move forgery detection using a dataset of 50 digital images. We compare the performance of four LBP-based methods, namely LBP, SIFT and SURF using metrics such as accuracy, precision, recall, and F1-score. Our results show that LBP outperforms the other methods in terms of accuracy and F1-score, while SIFT has the highest precision and recall. We also investigate the effect of various parameters, such as patch size and threshold values, on the performance of LBP. Our study provides valuable insights into the strengths and weaknesses of LBP-based methods for copy-move forgery detection, which can guide future research in this area. This study evaluates the performance of Local Binary Patterns (LBP) for detecting copy-move forgery in digital images. LBP is a widely used feature extraction technique in image processing and has been applied to various computer vision tasks, including forgery detection. The comparative study involves analyzing the accuracy, precision, recall, and F1-score of LBP and other popular forgery detection techniques, including SIFT and SURF, using a dataset of 50 digital images. The results show that LBP performs better than the other techniques, achieving an accuracy of 96.6%, precision of 94.0%, recall of 100%, and F1-score of 96.9%. This study provides useful insights for researchers and practitioners in the field of forgery detection, particularly for those interested in using LBP as a feature extraction technique.

IMAGE FORGERY DETECTION USING ADAPTIVE OVER-SEGMENTATION AND FEATURE POINT MATCHING USING MATLAB

Digital image forgery has gotten easier to do as computer technology and image processing tools have advanced. The validity of digital images, however, is a significant problem because they are a common source of information. The adaptive over-segmentation and feature point matching approach outlined below is a recommended technique for identifying fake images using a Matlab software method. Block- based and keypoint-based forgery detection techniques are both integrated into the suggested scheme. First, the proposed Adaptive Over-Segmentation algorithm adaptively and non-overlappingly segments the host picture into irregular blocks using image processing methods. Once the feature points have been extracted from each block as block features, the block features are compared to one another to locate the labeled feature points. This process can roughly identify the regions where a forgery is believed to have taken place. To produce the identified forgery regions, it then applies the morphological operation to the merged regions. The experimental findings show that, in comparison to the current state-of-the-art copy-move forgery detection methods, the proposed copy-move forgery detection scheme can produce significantly better detection results even under a variety of difficult conditions. KEYWORDS: Matlab, Adaptive over-segmentation, Colour block feature, Image matching

Comparison of specific segmentation methods used for copy move detection

<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>

A comparative study on image forgery-facial retouching

Forgery with the digital images is being very easy now days due to the very advanced and open source image editing tools, software and devises which supports a high quality of resolutions. Tempering with digital documents for changing identity or sometimes for fun is increasing day by day as the era is of digital world. Detecting clues of tampering and verifying the authenticity of images is an important issues now-a-days and growing research field. The existing research in the area of digital image forgery identification is discussed here. Different types of image forgery attacks along with its detection and classification are discussed too. A general review of facial image authentication is studied and discussed here along with the current methods and techniques for the detection and classification. A comparative analysis is presented here for the researchers as a mind mapping to work in the same field.

Detection of tamper forgery image in security digital mage

Every industry that uses digital photos is concerned about image security. Forensics and public safety have long relied on suspect photos, crime scene photos, biometric photos, and other images. As digital imaging has advanced, the use of digital images iun this field has grown significantly. While digital image processing has helped to develop many new approaches in forensic research, it has also simplified image manipulation. The public availability of various snipping image manipulation software has made digital image validity a problem. It's used as strong evidence in a variety of crimes, as well as documentation for a variety of reasons. The advancement of photo processing and editing software has simplified and made it more accessible to create and modify photographs. The most common types of picture forgery are copy-move forgery and splicing images. To conceal or display an error scenario, a portion of a photograph is duplicated and pasted further in the photograph, and splicing an image means two images in one image. This research looks at different types of digital image forgeries as well as forgery detection software. A review of existing approaches for detecting faked images was conducted.

Gray Level Co-occurrence Matrix with Binary Robust Invariant Scalable Keypoints for Detecting Copy Move Forgeries

With advancement in technology, especially in imaging field, digital image forgery has increased a lot nowadays. In order to counter this problem, many forgery detection techniques have been developed from time to time. For rapid and accurate detection of forged image, a novel hybrid technique is used in this research work that implements Gray Level Co-occurrence Matrix (GLCM) along with Binary Robust Invariant Scalable Keypoints (BRISK). GLCM significantly extracts key attributes from an image efficiently which will help to increase the detection accuracy. BRISK is known to be one of the 3 fastest modes of detection which will increase the execution speed of GLCM. BRISK even processes scaled and rotated images. Then the Principal Component Analysis (PCA) algorithm is applied in the final phase of detection will remove any unrequited element from the scene and highlights the concerned forged area.

ASCA-squeeze net: Aquila sine cosine algorithm enabled hybrid deep learning networks for digital image forgery detection

The basic element in resolving numerous challenges, particularly social concerns like those in court cases and Facebook, is image forgery detection. The primary objective of this study is to build and develop an effective system for detecting digital image forgery utilising the recently proposed technique called the Aquila Sine Cosine Algorithm (ASCA). The forgery from the digital image is detected in this study using a hybrid deep learning technique that incorporates Deep Convolutional Neural Network (DCNN) and Squeeze Net. Additionally, the training time and computational complexity of the detection process are decreased by updating the weight of both the DCNN and the Squeeze Net using the developed ASCA technique. Additionally, the developed ASCA is produced by combining the update functions of the Aquila Optimizer (AO) with the Sine Cosine Algorithm (SCA). As a result, the hybrid deep learning classifier provides the classified output as either the authentic image or the forged image using a copy-move forgery detection dataset. The experimentation of the developed model has provided higher performance, as shown by testing accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) of 0.980, 0.976, and 0.956, respectively. Furthermore, by varying the iteration, testing accuracy, TNR, and TPR obtained by the devised technique are 0.944, 0.947, and 0.936, and by varying the population size obtained testing accuracy values of 1, TNR values of 1.003, and TPR values of 0.991, respectively, by algorithmic analysis.

The detection of copy move forgery image methodologies

In every area that uses digital photos, image security is a major concern. Photos of suspects, crime scene photos, biometric photographs, and other images have long been used in forensic investigations and public safety. With the evolution of digital imaging, the digital images used in this area have increased significantly. As much as digital image processing has facilitated the development of numerous new approaches in forensic research, it has also made image manipulation simple. Digital image validity has become a problem with the public availability of various snipping image manipulation software. As a result, the need for image tampering detection software has become essential. Digital photographs have become widely used in past years for a variety of applications. The material will be disseminated through print media, the website, as well as scholarly publications. It is utilized as powerful evidence in numerous offenses as well as documentation for something like a range of functions The introduction to the photo processing and editing tools has made it simple and accessible to create or change photographs. Are of the most frequent kind of picture forgery is a copy-move forgery, in which a portion like a photographic is duplicated as well as pasted further in a photo to conceal at some point crucial or display an error scenario. This study examines many sorts of digital image forgeries as well as the detection of forgery tools. A review of available approaches for faked images was conducted.

A Thorough Investigation on Image Forgery Detection

Image forging is the alteration of a digital image to conceal some of the necessary or helpful information. It cannot be easy to distinguish the modified region from the original image in some circumstances. The demand for authenticity and the integrity of the image drive the detection of a fabricated image. There have been cases of ownership infringements or fraudulent actions by counterfeiting multimedia files, including re-sampling or copy-moving. This work presents a high-level view of the forensics of digital images and their possible detection approaches. This work presents a thorough analysis of digital image forgery detection techniques with their steps and effectiveness. These methods have identified forgery and its type and compared it with state of the art. This work will help us to find the best forgery detection technique based on the different environments. It also shows the current issues in other methods, which can help researchers find future scope for further research in this field.

A robust detection and localization technique for copy-move forgery in digital images

Image forensic analysis becomes a major role in the field of digital image security due to tampering and forgery. The image forgery violates the authenticity and ownership of digital images. Copy-move forgery considers a significant kind of image forensic analysis algorithm. In this kind, the forger copies a part of an original image and then pastes it into the selected position from the same image. The purpose of forgery is to hide or highlight a specific region of the original image. To detect copy-move forgery, there are two traditional techniques: block-based and keypoint-based. The main drawback of the keypoint-based technique is the insufficient features for the small and flat regions, which causes undetected forgery. In contrast, the block-based technique has intensive processing. Therefore, this paper proposes a robust scheme that overcomes the drawbacks of the above techniques and maintains their advantages. This scheme adopts three connected stages, the first detects the initial duplicated regions using the SURF-HOG detector and descriptor. Subsequently, the second stage localizes the primary matched regions by SLIC segmentation and then selects the suspicious neighbor regions to be combined with primary regions to obtain the active regions. In the third stage, the block-based technique adopts overlapping Zernike moments to extract sufficient key points from the produced active regions. In the final stage, the duplicated regions are classified into authentic or forged regions. The proposed scheme provides not only forgery detection but also localization and recognition for the duplicated regions. The experimental results show that the proposed scheme is fast and has high accuracy for forgery detection and localization, at least 93.75, and 7.25 in terms of True Positive and False Positive Rates. Moreover, the scheme has high robustness under various conditions and attacks such as geometric transformation attacks and compound photometric attacks. The proposed scheme can be used in sensitive applications such as cybercrime detection and adopted as evidence in the courts.

Digital image forgery: taxonomy, techniques, and tools–a comprehensive study

Digital image forgery: taxonomy, techniques, and tools–a comprehensive study