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In the digital age, images are widely used to document events, provide evidence, and communicate information. Ensuring the authenticity of digital images is crucial to maintaining trust and integrity in various domains, including journalism, forensics, legal proceedings, and historical documentation. In this research, a PeCA-DCNN model for digital image detection is proposed, employing a hybrid approach. Initially, the data from image forgery databases is collected and preprocessed to eliminate noise and artefacts. Subsequently, the Viola–Jones algorithm detects frontal faces, and feature extraction is performed using pre-trained models VGG-16 and Resnet-101. To reduce the computational overhead, feature extraction is performed and generates a feature vector. The PeCA algorithm, combined with an adaptive self-boosted DCNN, is used to classify fake and genuine images. The PeCA algorithm enhances model performance by adjusting classifier parameters’ weights and biases. When evaluating the PeCA-DCNN, significant improvements in accuracy, sensitivity, and specificity are obtained with enhancement rates of 1.48, 3.06, and 0.05 in an 80% training scenario, and 3.92, 3.24, and 2.22 in k-fold cross-validation. These results demonstrate the effectiveness of the proposed approach compared to existing techniques. [ABSTRACT FROM AUTHOR]

The rapid evolution of technology has raised questions about the authenticity of digital images due to easy access to software for image manipulation. This challenges the reliability of cybernetic images, making it difficult for analysts to verify their authenticity. Therefore, there is a need to improve image forgery detection techniques. Many existing systems rely on standard pooling and a single approach, limiting the comprehensiveness and demonstration of features acquired. In response, this research introduces a Pixel-Based Optimized Deep Convolutional Neural Network (PBO-DCNN) classification and a Hybrid Boosting Machine method for detecting image fabrications. The technique employs blockwise feature extraction in a block-by-block manner and a Hybrid Boosting Machine segmentation approach to enhance detection. Performance analysis shows that the Hybrid Boosting Machine approach attained an accuracy of 94.74%, a sensitivity of 95.00%, and a specificity of 95.27% on the DSO-1 dataset. Further, the Hybrid Boosting Machine model attained the accuracy, sensitivity, and specificity values of 94.86%, 94.97%, and 93.38% on the DSI-1 dataset concerning the training percentage (TP). Comparative analysis against traditional techniques such as Linear Regression (LR), Deep CNN, SVM, Decision Tree (DT), and Naïve Bayes (NB) classifiers demonstrates the superior performance of the Hybrid Boosting Machine approach. [ABSTRACT FROM AUTHOR]

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Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of various methodologies in the field was created. Unlike previous studies that focused on picture splicing or copy-move detection, this study intends to investigate the universal type-independent strategies required to identify image tampering. The work provided analyses and evaluates several universal techniques based on resampling, compression, and inconsistency-based detection. Journals and datasets are two examples of resources beneficial to the academic community. Finally, a future reinforcement learning model is proposed. [ABSTRACT FROM AUTHOR]

Summary: The 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The rapid advancement of science and technology has led to the potential for easy manipulation of multimedia content through the use of diverse editing tools. This poses a significant threat to the credibility and integrity of multimedia information. Consequently, substantiating digital images is becoming gradually crucial as digital images hold vital information and are used as essential pieces of evidence in various sectors. The necessity and relevance of digital image forensics have drawn several academics to develop various detection procedures in image forensics. Passive image forgery detection is the foundation of image forensics. Some common passive forgeries that influence the image's authenticity are image splicing, copy-move, and retouching. In recent times, substantial research effort has been devoted to developing novel approaches for detecting several image forgeries. This study provides an overview of similar research efforts that have been carried out utilizing a well-defined methodology. Our goal is to create an efficient way for image forensics researchers to discover new features of forgeries. This study presents a brief introduction to image forensics, including a historical perspective, taxonomy, and framework of image forgery detection approaches. Various resources useful to academic researchers, such as journals, datasets, websites, and performance parameters are explored and presented. This paper will provide a comprehensive review that will aid researchers in overcoming the numerous challenges experienced in earlier studies. Also, future directions are provided to help scholars in this domain. The purpose of this research is to evaluate passive image forgery detection approaches, therefore benefiting new researchers. [ABSTRACT FROM AUTHOR]

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named "bounded generalized Gaussian mixture model". The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images. [ABSTRACT FROM AUTHOR]

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic. One of the most popular methods for manipulating digital images is image splicing, which involves copying a specific area from one image and pasting it into another. Attempts were made to mitigate the effects of image splicing, which continues to be a significant research challenge. This study proposes a new splicing detectionmodel, combining Sonine functions-derived convex-based features and deep features. Two stages make up the proposed method. The first step entails feature extraction, then classification using the "support vector machine" (SVM) to differentiate authentic and spliced images. The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels. The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset "Chinese Academy of Sciences, Institute of Automation" which is a publicly available dataset for forgery classification. The experimental results show that, for image splicing forgery detection, the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy, precision, and recall. Overall, the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations. Finding the regions or locations where image tampering has taken place is limited by the study. Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions. [ABSTRACT FROM AUTHOR]

With advent of digital devices, we are surrounded by many digital images. We usually believe on digital images in whatever form presented to us. Therefore, we need to be careful as the images may be forged. There exist several image forgeries through which original intent of the image may be hidden and some other meaning is reflected through forgery. Copy-move forgery is one such forgery technique, where the manipulator copies certain portion of the image and duplicates it in some other portion of the same image. In this paper, we propose a novel approach to detect the copy-move forgery in images using non-overlapping block level pixel comparisons and that can achieve better detection and classification accuracy. This approach divides image into 4, 5, 6 or more such blocks and compare each block by moving sliding window over the entire image which is not overlapping with current block. It was found that with different number of blocks the forged region of different sizes can be easily found. We have used SSIM (structure similarity index) parameter to classify the image as forged or original. Algorithm is simulated on various datasets including (MICC, CASIA, coverage, and COMOFOD, etc.) and achieved maximum accuracy of 98% and also compared our result on precision, recall, FPR and FNR including other parameters. [ABSTRACT FROM AUTHOR]

Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified. In the current cyber world where deepfakes have shaken the global community, confirming the legitimacy of a digital image is of great importance. With the advancements made in deep learning techniques, now we can efficiently train and develop state-of-the-art digital image forensic models. The most traditional and widely used method by researchers is convolution neural networks (CNN) for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training. Therefore, in this study, a transfer learning based deep learning technique for image forgery detection is proposed. The proposed methodology consists of three modules namely; preprocessing module, convolutional module, and the classification module. By using our proposed technique, the training time is drastically reduced by utilizing the pre-trained weights. The performance of the proposed technique is evaluated by using benchmark datasets, i.e., BOW and BOSSBase that detect five forensic types which include JPEG compression, contrast enhancement (CE), median filtering (MF), additive Gaussian noise, and resampling. We evaluated the performance of our proposed technique by conducting various experiments and case scenarios and achieved an accuracy of 99.92%. The results show the superiority of the proposed system. [ABSTRACT FROM AUTHOR]

Protecting the data against forgery is an important concept and digital images are necessary for exhibiting information. Digital image forgeries are attaching extraordinary patterns to original images and it causes visual heterogeneousness. Image copy-move forgery is a challenging technique, that involves copying part of an image and then pasting the copied part into the same image. In this paper, the golden jackal optimization (GJO) is proposed for feature selection and multi-support vector machine (M-SVM) is proposed for effective classification. The GJO optimizes the feature selection process by iteratively searching for the most informative subset of features. By leveraging the exploration and exploitation of GJO, the algorithm efficiently explores the feature space, selecting relevant features that contribute to accurate forgery classification. This enhance the performance of MSVM by focusing on the most discriminative features. For this work, a dataset named as MICC-F2000 is examined which is managed by 2000 images in this 1300 are original and 700 are forged images. The result shows that the proposed GJO based MSVM model delivers the performance metrics like accuracy, sensitivity, specificity, precision, and MCC values about 99.47 %, 97.01%, 96.51%, 99.62%, and 96.39% respectively which ensures accurate forgery detection compared with existing methods such as SSDAE-GOA-SHO, ConvLSTM, CNN and dual branch CNN methods. [ABSTRACT FROM AUTHOR]

Digital image forgeries become one of the most challenges faced by technology users today. A copy-move forgery is one of the most common types of these forgeries. This type is not only common, but it is also very simple to execute to create deceptive images. Many algorithms use various technologies to create a high-performance Copy Move Forgery Detection (CMFD) system. There are currently two major CMFD trends: traditional CMFD algorithms and deep-learning-based CMFD algorithms. The main concept behind CMFD algorithms is to extract image features using various techniques and then search for matching features. This main idea can be carried out by using different feature extraction techniques with various matching techniques. For traditional algorithms, there are many types of features extractors used such as invariant image moments, texture and intensity descriptors, Scale-Invariant Feature Transform (SIFT), Speed Up Roust Features (SURF), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), Principal Component Analysis (PCA), and other extractors. The strength of these extractors varies according to the image properties and the forgery's performance perfection. Deep learning-based CMFD algorithms operate as a black box, by extracting image features using layers with complex training and validation processes to achieve the best scenario, such as Convolutional Neural Network (CNN), Convolutional Long Short-Term Memory (CovLSTM), Multi-domain CNN, and Buster-Net. This paper discusses different types of digital image forgery with focusing on copy-move forgery. A detailed survey of various types of CMFD algorithms, on the other hand, has been displayed. The paper provides a comprehensive comparison of various algorithms and evaluates them using various types of evaluation parameters. Finally, the paper focuses on the different datasets used by various algorithms to evaluate their performance accuracy and testing time. These datasets are detailed in terms of image numbers, the number of original images, the number of forged images, the resolution variety in each dataset, the size of the tampered region in each dataset, and the strength of each dataset compared with each other. [ABSTRACT FROM AUTHOR]

In modern society, digital images can be far-reaching, and the images are manipulated by various software and hardware technologies. The image forgery activities are undertaken by the attackers mainly for damaging the reputation of people or receiving fiscal gain, etc. Taking this into consideration, many techniques are developed to detect the forged images. In this paper, a new deep learning-based approach is introduced for copy-move forgery detection. The input images are segmented into non-overlapping patches using superpixel-based modified dense peak clustering and the features are extracted from the segmented patches by applying deep learning structure of attention-based DenseNet 121 model. Besides, to compare every block, the depth of each pixel is reconstructed, and eventually matching process is carried out using the adaptive chimp patch matching approach, which detects the suspicious forged regions in an image. Finally, the matched keypoints are merged with the segmented patches using the merged keypoint matching algorithm. As a result, the new deep learning approach has detected the forged regions efficiently from the tampered image with less time even the image is compressed, rotated, or scaled. The performance is evaluated in terms of recall, precision, accuracy, F1-score, computational time, and False Positive Rate (FPR). Moreover, the performance is compared with the other existing approaches, and the outcomes showed that the proposed method has achieved higher accuracy of 97%, recall of 99%, precision of 97.84%, F1-score of 98.81%, FPR of 0.022 and less computational time of 2.5 s. [ABSTRACT FROM AUTHOR]

The rapid pace of the digital age has led to an increase in the illegal copying, reproduction, and creation of forgeries of digital images. Copy-move forgery is one of the most common image forgery techniques which is used for tampering with image content. In this paper, a novel scheme is proposed to detect copy-move forgery and if the image is forged, it is aimed to reveal duplicated regions. This study is based on the combination of a keypoint-based method that fails especially when a high rate of blurring attack but is successful in geometric transformation attacks, and a segmentation-based method proposed to gain resistance to blurring attack. The method firstly queries the presence of a blurring attack in the suspicious image. In case of the presence of this attack, forgery detection is made with the segmentation-based module and in the other case with the keypoint-based module. In the keypoint-based module, in order to minimize the effect of possible noise addition attacks applied to the image, a denoising step is performed with the deep neural network. After that in this module, it is proposed to use AKAZE keypoints to reveal duplicated regions, and in the tamper localization step, the Ciratefi-based approach is applied. In the segmentation-based step, it is proposed to segment input images with two-layered segmentation with entropy-based and color-based segmentation. Then, DCT based features are extracted from the image sub-blocks in the segments, the blocks with the same segment label are matched with the feature vectors obtained among themselves. The experimental results demonstrate that the proposed method has an overall performance that is superior to popular approaches on two open-access copy-move forgery datasets. [ABSTRACT FROM AUTHOR]

Digital images have become an important carrier for people to access information in the information age. However, with the development of this technology, digital images have become vulnerable to illegal access and tampering, to the extent that they pose a serious threat to personal privacy, social order, and national security. Therefore, image forensic techniques have become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely applied in the field of image forensics and the performance achieved has significantly exceeded the conventional forensic algorithms. This survey compares the state-of-the-art image forensic techniques based on deep learning in recent years. The image forensic techniques are divided into passive and active forensics. In passive forensics, forgery detection techniques are reviewed, and the basic framework, evaluation metrics, and commonly used datasets for forgery detection are presented. The performance, advantages, and disadvantages of existing methods are also compared and analyzed according to the different types of detection. In active forensics, robust image watermarking techniques are overviewed, and the evaluation metrics and basic framework of robust watermarking techniques are presented. The technical characteristics and performance of existing methods are analyzed based on the different types of attacks on images. Finally, future research directions and conclusions are presented to provide useful suggestions for people in image forensics and related research fields. [ABSTRACT FROM AUTHOR]

This paper proposes an overlapping block-based passive forensic scheme for copy–move forgery detection in digital images that works on a reduced search space. It uses a Gaussian image pyramid to generate and analyze input images at different resolutions. The conventional overlapping block-based procedures produce satisfactory results but are highly compute-intensive for large and medium-sized images. An increase in image size leads to a rapid rise in the number of overlapping blocks in the image, making processes like feature extraction, matching, and shift-vector calculations very expensive. The proposed approach initially performs relative forgery detection through block-wise processing of lower resolution components of the original image. In this process, discrete cosine transform is used to extract significant coefficients from each block and further analyzed to identify forgeries relatively in the selected lower resolution components. This process aids in selecting a smaller search space for potentially forged areas in the original image. Finally, the actual forgery detection is performed on this reduced search space, decreasing the computational overhead while maintaining accuracy in the results. The proposed procedure also shows robustness against various attacks and post-processing operations. [ABSTRACT FROM AUTHOR]

Recently, forensic tools have been presented for detecting the forged image and illegal image manipulations. This paper presents an efficient and simple image contents verifying approach acting as a forensic technique. The presented approach mechanism is built by adding a hidden mark in a secret images, this mark achieves image integrity verification and detecting the tampering or forgery in the secret/authentic images. The secret/authentic/authentic image is divided to two main partitions, each portion is segmented to small blocks. These blocks in one partition are used to mark the blocks of the second partition using data transform technique. Firstly, the sensitive image is marked according to a self-embedding method. Then, a transform domain is utilized in order to embed a block-based signature into another block of the same image. Common discrete transform domains like DWT, DCT, and DFT are examined individually. Different analyses and comparison measurements are employed. The DCT is proved to be the most suitable and efficient transform domain to be used with the proposed scheme. At the receiver side, the reverse process is performed to verify image integrity. As proved from the experiments, this mark-algorithm is not visible or observable and robust against various attacks. [ABSTRACT FROM AUTHOR]

Copy move forgery in digital images became a common problem due to the wide accessibility of image processing algorithms and open-source editing software. The human visual system cannot identify the traces of forgery in the tampered image. The proliferation of such digital images through the internet and social media is possible with a finger touch. These tampered images have been used in news reports, judicial forensics, medical records, and financial statements. In this paper, a detailed review has been carried on various copy-move forgery detection (CMFD) and localization techniques. Further, challenges in the research are identified along with possible solutions. [ABSTRACT FROM AUTHOR]

Recent developments in technology and the area of digital communications have rendered digital images increasingly susceptible to tampering and alteration by persons who are not authorized to do so. This may appear to be acceptable, especially if an image editing process is necessary to delete or add a particular scene that improves the quality the image. But what about images used in authorized governmental transactions? The consequences can be severe; any altered document is considered forged under the law and may cause confusion. Also, any document that cannot be verified as being authentic is regarded as a fake and cannot be used, inflicting harm on people. The suggested work intends to reduce fraud in electronic documents using a novel generator. It employs modern encryption methods to conceal the data represented by the watermark and hide it secretly inside the document so that it is easy to check for signs of counterfeiting; thus, allowing for the discovery of forged papers [ABSTRACT FROM AUTHOR]

Copy–move forgery is one of the most common image forgeries in digital images. In copy–move forgery, some object or region of an object is copied and duplicated in other parts of the same image. Hence, there is a need to develop an accurate and robust forgery detection approach for various image forensics applications. In this article, an improved salient keypoint selection approach for copy–move forgery detection has been proposed. Scale-Invariant Feature Transform (SIFT) and KAZE keypoint features have been extracted from the input image and salient keypoints have been selected for improving the robustness of the proposed algorithm. Salient keypoint selection reduces the feature descriptor matching time for finding region duplication in the given image. To improve the detection accuracy of the proposed approach, selective search-based region proposals have been introduced to create a bounding box on the input image. Feature descriptor matching is performed between keypoints which are located inside two different bounding boxes. The proposed approach has been evaluated on two benchmark datasets, CoMoFoD and MICC-F220 and detection results outperformed state-of-the-art techniques under different geometric transformations and post-processing operations. [ABSTRACT FROM AUTHOR]

Digital images have become widespread in modern life, and they can be modified and produced using an inclusive range of software and hardware tools. Since, digital image forgery can be extremely damaging, thus, understanding the detection and classification of authentic and forged images is of great significance. Without diminishing the importance of other types of forgeries, copy-move forgery (CMF) can be considered among the most widely utilized forgeries because of its ease of implementation. Nowadays, deep learning-based approaches are considered up-to-date for the classification and detection of image forgery owing to their improved accuracy and automated feature extraction skills. A deep learning CMF detection framework is proposed in this paper, which classifies images as authentic or forged using a contrast-limited adaptive histogram equalization (CLAHE) and convolutional neural network (CNN). The CLAHE algorithm makes the hidden features of the image visible, as some of them are hard to detect in CMF. The effectiveness of proposed structure is appraised using benchmark datasets: GRIP, MICC-F2000, IMD and MICC-F220. In terms of various performance metrics, the experimental study demonstrates the effectiveness of the proposed approach among other approaches. Also, the robustness of the proposed technique is demonstrated against several geometrical attacks like scaling, noise addition, JPEG compression, and rotation. [ABSTRACT FROM AUTHOR]

Copy–move forgery is one of the most frequently used methods for producing fake digital images. Current algorithms for copy–move forgery detection (CMFD) cannot combine high accuracy and fast speed. Motivated by the observation, we propose a novel CMFD algorithm whose workflow is as follows. First, we use a keypoint-extraction method with the lowest contrast threshold to extract more keypoints from the input image. Second, a new technique, gradient-hash matching, finds pairs of similar keypoints quickly and effectively using a hash table, where the hash value is computed using gradients of keypoints. Subsequently, a new method called simplified cluster-based filtering exploits the density pattern of keypoints in the copy–move regions to remove false matching keypoint pairs. Finally, image matting is applied to indicate the forgery regions vividly. Extensive experiments show that not only the new algorithm is better than the state-of-the-art algorithms in terms of computation correctness, but also its computation time is drastically less. Commonly only about half time is needed. The relative time saving is even higher when images are larger. Different algorithms modules are compared through experiments to choose the best combination. [ABSTRACT FROM AUTHOR]

Digital image forgery has become one of the serious issues in today's era. Digital images can be forged in several ways. Image splicing is a simple and most commonly used forgery technique. In image splicing, two or more images are used to create a single composite image. However, the detection of image splicing forgery is not easy. Motivated by the fact that the images captured from different devices show different noise levels, this paper proposes a new method to detect and localize the image splicing forgery based on noise level estimation. In the proposed method, initially, the input image is divided into irregular-shaped superpixel blocks using the Simple Linear Iterative Clustering technique. Secondly, the PCA-based image estimator is used to estimate the noise level from the superpixel blocks. Finally, the k-means clustering technique is used to cluster the blocks into authentic and spliced blocks based on the noise levels. The experimental results performed on the CUISDE dataset demonstrate that the proposed method can localize the image splicing forgery with better accuracy as compared to existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The unprecedented growth in the easy availability of photo-editing tools has endangered the power of digital images. An image was supposed to be worth more than a thousand words, but now this can be said only if it can be authenticated or the integrity of the image can be proved to be intact. In this paper, we propose a digital image forensic technique for JPEG images. It can detect any forgery in the image if the forged portion called a ghost image is having a compression quality different from that of the cover image. It is based on resaving the JPEG image at different JPEG qualities, and the detection of the forged portion is maximum when it is saved at the same JPEG quality as the cover image. Also, we can precisely predict the JPEG quality of the cover image by analyzing the similarity using Structural Similarity Index Measure (SSIM) or the energy of the images. The first maxima in SSIM or the first minima in energy correspond to the cover image JPEG quality. We created a dataset for varying JPEG compression qualities of the ghost and the cover images and validated the scalability of the experimental results. We also, experimented with varied attack scenarios, e.g. high-quality ghost image embedded in low quality of cover image, low-quality ghost image embedded in high-quality of cover image, and ghost image and cover image both at the same quality. The proposed method is able to localize the tampered portions accurately even for forgeries as small as 10 × 10 sized pixel blocks. Our technique is also robust against other attack scenarios like copy-move forgery, inserting text into image, rescaling (zoom-out/zoom-in) ghost image and then pasting on cover image. [ABSTRACT FROM AUTHOR]

With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. Image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the state-of-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recently, digital images have been considered the primary key for many applications, such as forensics, medical diagnosis, and social networks. Image forgery detection is considered one of the most complex digital image applications. More profoundly, image splicing was investigated as one of the common types of image forgery. As a result, we proposed a convolutional neural network (CNN) model for detecting splicing forged images in real-time and with high accuracy, with a small number of parameters as compared with the recently published approaches. The presented model is a lightweight model with only four convolutional layers and four max-pooling layers, which is suitable for most environments that have limitations in their resources. A detailed comparison was conducted between the proposed model and the other investigated models. The sensitivity and specificity of the proposed model over CASIA 1.0, CASIA 2.0, and CUISDE datasets are determined. The proposed model achieved an accuracy of 99.1% in detecting forgery on the CASIA 1.0 dataset, 99.3% in detecting forgery on the CASIA 2.0 dataset, and 100% in detecting forgery on the CUISDE dataset. The proposed model achieved high accuracy, with a small number of parameters. Therefore, specialists can use the proposed approach as an automated tool for real-time forged image detection. [ABSTRACT FROM AUTHOR]

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition (SVD). It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size. At each step, the SVD is determined. First, the diagonal matrix's maximum value (norm) is selected (representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled). Then, the similar norms are grouped, and each leading group is separated into many subgroups (elements of each subgroup are neighbors) according to 8-adjacency (the subgroups for each leading group must be far from others by a specific distance). After that, a weight is assigned for each subgroup to classify the image as forgery or not. Finally, the F1 score of the proposed system is measured, reaching 99.1%. This approach is robust against rotation, scaling, noisy images, and illumination variation. It is compared with other similarmethods and presents very promised results. [ABSTRACT FROM AUTHOR]

Image forensics is an investigation of digital images to identify manipulations that have been done on them. Nowadays, due to the availability of different low-cost devices for capturing images, digital images are gaining quite a bit of popularity. It occurs frequently that these images are manipulated by mistake or on purpose, resulting in inaccurate information being presented by the image. There is a need to develop techniques to identify forgeries present in digital images used by the media, in court trials, and for maintaining visual records, since digital images are commonly used as evidence through the media, in court, and for maintaining record keeping. A detailed review of various image forgery detection techniques is presented in this article including comparisons between the various methods, pros and cons, and results obtained during the experimentation. [ABSTRACT FROM AUTHOR]

Nowadays, digital images have been manipulated for various fraudulent purposes from time to time. Therefore, proposing a detection algorithm for such manipulations has become a key interest for investigators. Digital image splicing and copy-move are among the most commonly performed forgeries and their detection is problematic. In spite of the number of accessible potential solutions for manipulation detection, the increasing number of forgery cases and their complexity necessitate the need for improved detection methods. The technique put forward in this article aims to deliver a reliable remedy for image manipulation detection. In the presented article, a novel procedure for discovering forgery in digital images is proposed with the use of quaternions that expands the distinction between probability distributions of authentic and forged images. Quaternions help in representing the image in its entirety as a vector field and discrete cosine transform is applied on the quaternion that is constructed from the color components of the image. After that, Markov features are originated from the transitional probabilities of the inter-block and intra-block correlation amongst the coefficients of the blocks along four directions. Finally, a feature vector of 648 dimensions is classified using a fusion of various classifiers. The performance of the approach is presented in terms of seven different performance metrics. The experiments are performed on three benchmark datasets viz. CoMoFoD dataset, CASIA version 1.0 and CASIA version 2.0 datasets. The proposed approach beats existing approaches over the CoMoFoD dataset and yields competitive outcomes for the CASIA version 2.0 dataset. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content. An effective technique for tampering the identification is the copy-move forgery. Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification. Contrastingly, deep learning (DL) models have demonstrated significant performance over the other statistical techniques. With this motivation, this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection (ODTLCMFD) technique. The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered, and then localize the copy moved regions. To perform the feature extraction process, the political optimizer (PO) with Mobile Networks (MobileNet) model has been derived for generating a set of useful vectors. Finally, an enhanced bird swarm algorithm (EBSA) with least square support vector machine (LS-SVM) model has been employed for classifying the digital images into the original or the forged ones. The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine (MSVM) technique and thereby enhance the classification performance. For ensuring the enhanced performance of the ODTL-CMFD technique, a series of simulations have been performed against the benchmark MICC-F220, MICCF2000, and MICC-F600 datasets. The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures. [ABSTRACT FROM AUTHOR]

Digital images are tampered easily but detection of non-uniform texture is a challenging task. Therefore, various schemes are developed from many years by numerous researchers to overcome issues related to image forgery but still it requires reliability and validity to process in future for civilization safe and secure online/offline communication. Therefore, we worked on both categories of passive image forensics individually and then we applied the combined process-Block based and Key-Point based schemes in order to detect forgery in more efficient manner using segmentation algorithm with irregular blocks. Proposed algorithm includes cloning with different degree rotation for image forensic investigation. Proposed scheme is applied over many morphological operations, Post processing and invariant transform techniques to validate the results. This scheme gives consistent results in case of different image size, type of images, resizing, scaling, shifting, and multiple cloning with uniform and non-uniform textures. Proposed algorithm works under different test scenario and gives results in just 37 s to investigate the analysis with less computational efforts as it involves some manual calculation with large number of datasets used for experimental testing. Proposed work includes real time datasets for best result against latest State-of-art-Methodologies. Future scope can work on other type of forgeries like audio and video etc. [ABSTRACT FROM AUTHOR]

With the expeditious advancement in digital technology and image editing software, it has become easy to manipulate digital images. Copy-move is a common type of image forgery, which threatens the authenticity of digital images by copying and pasting a certain part of the image within the same image. The main focus of this paper is to detect single as well as multiple copy-move forgeries efficiently by combining block-based and keypoint-based detection approaches. The fusion of block-based detection techniques i.e. adaptive over-segmentation (AS) and keypoint-based detection techniques i.e. accelerated KAZE (AKAZE) and scale-invariant feature transform (SIFT) makes the proposed scheme robust against various geometrical attacks as well as less computationally expensive. Moreover, the input image is converted into the different color channels and then, Cr channel is used for further processing because it detects the tampering artifacts left in the image which cannot be observed by human eyes. Also, the copy-move forgery is detected more precisely even in smooth regions due to the extraction of an adequate number of key points. The experimental results are executed on MICC-F220, IMD, GRIP, and COVERAGE datasets, and various performance metrics like precision, recall, F1 score, and F2 score are evaluated. The experimental results show that the proposed technique is robust against various geometrical attacks i.e. rotation, scaling, JPEG compression, and noise addition, and proved better when compared with the other existing techniques. Moreover, to ensure the robustness of the proposed approach, the statistical analysis test using ANOVA and cross-dataset performance is evaluated. [ABSTRACT FROM AUTHOR]

The fake images and visuals can easily spread among social media users and they largely impact decisionmaking in society. Image forgery has become increasingly common as more non-professionals have access to image manipulation tools. These fake images are so sneaky that an ordinary person cannot guess them. Through social media, such photos are utilized to promote erroneous information in society. Image forgery detection is about segmenting the forged part from the images, primarily a region of interest. This paper suggests a unique method that depends on a dual attention network to detect forged segments. This network contains self-attention modules that contribute to extracting and matching features in the channel and spatial domains. These features help locate and identify the forged portions of digital images at various scales and channels. This experimental study uses typical datasets such as CASIA V1.0, CASIA V2.0, and Columbia. Proposed IFLNet technique outperforms other advanced techniques with a precision of 96 %, recall rate of 95 %, accuracy rate of 98 %, F1-score of 96 % and IoU score of 92 % for Columbia dataset and correspondingly other two datasets also. [ABSTRACT FROM AUTHOR]

Today, manipulating, storing, and sending digital images are simple and easy because of the development of digital imaging devices from hardware and software points of view. Digital images are used in different contexts of people's lives such as news, forensics, and so on. Therefore, the reliability of received images is a question that often occupies the viewer's mind and the authenticity of digital images is increasingly important. Detecting a forged image as a genuine one as well as detecting a genuine image as a forged one can sometimes have irreparable consequences. For example, an image that is available from the scene of a crime can lead to a wrong decision if it is detected incorrectly. In this paper, we propose a combination method to improve the accuracy of copy–move forgery detection (CMFD) reducing the false positive rate (FPR) based on texture attributes. The proposed method uses a combination of the scale-invariant feature transform (SIFT) and local binary pattern (LBP). Consideration of texture features around the keypoints detected by the SIFT algorithm can be effective to reduce the incorrect matches and improve the accuracy of CMFD. In addition, to find more and better keypoints some pre-processing methods have been proposed. This study was evaluated on the COVERAGE, GRIP, and MICC-F220 databases. Experimental results show that the proposed method without clustering or segmentation and only with simple matching operations, has been able to earn the true positive rates of 98.75%, 95.45%, and 87% on the GRIP, MICC-F220, and COVERAGE datasets, respectively. Also, the proposed method, with FPRs from 17.75% to 3.75% on the GRIP dataset, has been able to achieve the best results. [ABSTRACT FROM AUTHOR]

Today, image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images. For the evaluation of the algorithm, CVLAB, CASIA V1, Columbia, and Columbia Uncompressed datasets were used. Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries. The algorithm detected all proposed forgery pixel methods with an accuracy of 91%. The main novelties of the proposal are the reduced number of features needed for identification and its robustness for the file format and image size. [ABSTRACT FROM AUTHOR]

Copy-move forgery detection is a common image tampering detection technology. In this paper, a novel copy-move forgery detection scheme is proposed. The proposed scheme is based on Regional Density Center (RDC) clustering and Refined Length Homogeneity Filtering (RLHF) policy. First, to obtain an adequate number of keypoints in smooth or small areas of the image, the proposed scheme employs scale normalization and adjustment of the contrast threshold of the input image. Subsequently, to speed up the feature matching process, a matching algorithm based on gray value grouping is used to match the keypoints. RLHF policy is applied to filter the mismatched pairs. To guarantee a good estimation of the affine transformation, the RDC clustering algorithm is proposed to group the matched pairs. Finally, the correlation coefficients are computed to precisely locate the tampered regions. The proposed copy-move forgery detection scheme based on RDC and RLHF can effectively identify duplicated regions of digital images. It demonstrates the effectiveness and robustness of the proposed scheme over many state-of-the-art schemes on public datasets. [ABSTRACT FROM AUTHOR]

The expressive ability of digital visual media with the simplicity of their acquisition, processing, distribution, manipulation, and storage is such that they are utilized more to convey information over other sources of the information carriers. Formerly and conventionally, there has been credence in the authenticity of digital visual media. Still, with the availability of inexpensive and easy to use digital devices with high-resolution cameras coming in handy in mobile phones and the availability of low-cost and user-convenient editing tools, visual media forgery is ubiquitous. In general, forgery introduces certain artifacts in digital images. The recapturing process eliminates those artifacts and misleads the forensic system. The research work proposed in this paper presents a novel technique for the detection of near-duplicate images by examining the edge profile obtained by the edge histogram descriptor. The difference between the numbers of grouped directional edges present in singly captured and the near-duplicate image is used to build the feature vectors. Based on the training of those feature vectors, a model is generated using the SVM classifier. The proposed method is tested on three datasets of high-resolution and high-quality near-duplicate images, namely, NTU-ROSE, ICL, and Mturk. The evaluated results exemplify that the technique proposed is comparatively better than the state-of-the-art methods for near-duplicate detection. Features extracted from the image of vector length 91 allows an SVM classifier to achieve the precision of 100% and selectivity value above 97%. Furthermore, our results show that the proposed method achieves a performance rate that exceeds the overall accuracy of 99% for all three datasets. [ABSTRACT FROM AUTHOR]

In recent years, one of the forgery detection methods is the Copy-Move Forgery Detection (CMFD), which is mostly used approach amongst all the forgery identification techniques. In some forgery identification schemes, it is rigid to distinguish falsified regions or real similar regions. However, detection of the tampered region is very difficult if the interacted area is rescaled or rotated in the existing tampering recognition algorithms. Hence, an Enhanced CMFD approach (ECMFD) is projected in this paper to tackle the difficulties experienced by the existing approaches. The proposed technique developed an innovative and efficient Machine Learning (ML) approach called Prewitt mm′ Filter with Generalized Action Selection based Hybrid Artificial Bee Colony African Buffalo Optimization (PFGAS based HABC-ABO). Initially, the proposed PF model is utilized for de-blurring and detecting the edges of the input images. Subsequently, the de-blurred images are separated into blocks using GAS, and the copy-move forged region in digital images under different scenarios such as scaling and rotation is detected using HABC-ABO. In this approach, a portion of the picture is copied and localized in another region in the corresponding picture for including or screening some valuable information that exists in the image. The simulation of this method is done with the use of Python, and the performance analysis indicates that the proposed PFGAS based HABC-ABO method has a better effect on rotation and scaling. Hence, it attained high performance in parameter metrics like accuracy, precision, and recall than other existing approaches. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Image forgery detection is a very challenging task now a day. Latest tools and applications make it easy. Artefact change our thought and perceptions. OBJECTIVES: A forgery detection system is a need of time to detect image forgery. METHODS: We proposed a blind image forgery detection technique. Optimal threshold-based Enhanced Local Ternary Pattern (OELTP) technique implemented on smoothed image. Features are extracted in the form of frequency to implement Discrete Wavelet Transform (DWT) on the chrominance component of the image. Support Vector Machine is used for classification. RESULTS: The accuracy of the forgery detection on the proposed technique is better than some of the previous states of work. CONCLUSION: Image forgery detection system performance has been improved by better localization of the forgery. Performance of the global threshold improved by using the latest technique, and reducing the operational complexity. [ABSTRACT FROM AUTHOR]

The paper considers the improvement of the method of detection and localization of cloning areas in digital images. A significant role in the modern world is played by digital signals, in particular, digital images used in science, medicine, court proceedings, the press, etc. Today, everyone has a camera, if the event is significant, you will have thousands of photos at your disposal. However, changing the photo is very easy. Even amateurs can do it. Often, such falsifications are almost impossible to detect with the naked eye. One of the most commonly used operations in the forgery of digital images is the cloning operation, in which a part of a digital image is replaced by a part of the same digital image. The paper considers the method of detection and localization of cloning regions based on the Pearson correlation coefficient. The basic method uses 8x8 blocks as a compromise between image processing time and the accuracy of cloning region detection. Increasing the block size will speed up the base method. Another way to improve the method of detecting and locating cloned blocks is to use markers. Markers are predefined pixels in a block that are used to define the perspective of the block to identify the cloning area. Markers for square blocks and blocks of complex shape are proposed in the work. Blocks of a complex shape in the work are considered to be blocks consisting of a suitable set of pixels, which are used to detect and localize areas of cloning. The main steps of the improved method of detection and localization of cloning regions in digital images are presented. Evaluation of the effectiveness of the improved method of detection and localization of cloning areas was performed by measuring the time of detection and localization of cloning areas before and after improvement. [ABSTRACT FROM AUTHOR]

There is an increase in the requirement of digital image authentication in law, journalism, and medicine, even in the industry. Copy move forgery is the most common method of forgery methods which are applied to digital images. The importance of verifying digital images that are used in important areas with real-time systems is increasing. Taking this need into consideration, in this study, a robust digital image copy move forgery detection method is proposed to realize in real time. The proposed method first extracts the textural form of the input image. The SIFT keypoints and descriptors are obtained from textual images thus more robust keypoints and descriptors will be utilized. Keypoint matching is realized to reveal the image is forged or not and suspicious regions are determined. The localization of forged pixel is realized via Ciratefi based approach. The post-processing step is applied to make the labeling pixels more accurate by utilizing Connected Component Labeling and morphological operation. The GRIP and CMH datasets are used to showing the effectiveness of the state-of-the-art and proposed method. The method is robust to geometric distortion attacks and image degradation attacks. The results indicate that the proposed method has the highest performance especially against geometric distortion attacks suck as rotation and scaling. [ABSTRACT FROM AUTHOR]

Analyzing digital images to reveal modifications is called image forensics. Digital images are now becoming incredibly popular due to the availability of several inexpensive image-capturing gadgets. These images are frequently altered, either unintentionally or intentionally, which causes the image to convey false information. Since digital images are frequently utilized as evidence in court proceedings, media, and for preserving visual records, approaches to detecting forgeries in these images should be designed. This paper thoroughly analyzes several image forgery detection strategies, including comparisons of the strategies, advantages, disadvantages, and experimental findings. [ABSTRACT FROM AUTHOR]

Recently, the technology of digital image forgery based on a generative adversarial network (GAN) has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye by compositing and editing a person's face or a specific part with the original image. Thus, much attention has been paid to digital image forgery as a social issue. Further, document forgery through GANs can completely change the meaning and context in a document, and it is difficult to identify whether the document is forged or not, which is dangerous. Nonetheless, few studies have been conducted on document forgery and new forgery-related attacks have emerged daily. Therefore, in this study, we propose a novel convolutional neural network (CNN) forensic discriminator that can detect forged text or numeric images by GANs using CNNs, which have been widely used in image classification for many years. To strengthen the detection performance of the proposed CNN forensic discriminator, CNN was trained after image preprocessing, including salt and pepper as well as Gaussian noises. Moreover, we performed CNN optimization to make existing CNN more suitable for forged text or numeric image detection, which have mainly focused on the discrimination of forged faces to date. The test evaluation results using Hangul texts and numbers showed that the accuracy of forgery discrimination of the proposed method was significantly improved by 20% in Hangul texts and 5% in numbers compared with that of existing state-of-the-art methods, which proved the proposed model performance superiority and verified that it could be a useful tool in reducing crime potential. [ABSTRACT FROM AUTHOR]

To detect the maliciously tampered region in digital images, this paper proposes an image forgery localization method based on a fully convolutional network (FCN). In the pre-processing phase of the network, noise features are used to adequately expose the subtle changes in the image caused by manipulation operations, thus enhancing the generalization ability of the network. The convolutional layer is used in a fully convolutional network instead of the fully connected layer to generate a pixel-wise prediction. In addition, the region proposal network used in object detection is added to improve the robustness. Experiments on standard datasets show that our method can accurately locate the tampered regions of images and improve generalization ability and robustness. [ABSTRACT FROM AUTHOR]