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Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This paper aims to propose a novel splicing detection method using a discriminative robust local binary pattern (DRLBP) with a support vector machine (SVM). Reliable detection of image splicing is of growing interest due to the extensive utilization of digital images as a communication medium and the availability of powerful image processing tools. Image splicing is a commonly used forgery technique in which a region of an image is copied and pasted to a different image to hide the original contents of the image.

The structural changes caused due to splicing are robustly described by DRLBP. The changes caused by image forgery are localized, so as a first step, localized description is divided into overlapping blocks by providing an image as input. DRLBP descriptor is calculated for each block, and the feature vector is created by concatenation. Finally, features are passed to the SVM classifier to predict whether the image is genuine or forged.

The performance and robustness of the method are evaluated on public domain benchmark data sets and achieved 98.95% prediction accuracy. The results are compared with state-of-the-art image splicing finding approaches, and it shows that the performance of the proposed method is improved using the given technique.

The proposed method is using DRLBP, an efficient texture descriptor, which combines both corner and inside design detail in a single representation. It produces discriminative and compact features in such a way that there is no need for the feature selection process to drop the redundant and insignificant features.

This study aims to solve problems of detecting copy-move images. With input images, the problem aims to: Confirm the original or forgery of the images, evaluate the performance of the detection and compare the proposed method’s effectiveness to the related ones.

This paper proposes an algorithm to identify copy-move images by matching the characteristics of objects in the same group. The method is carried out through two stages of grouping the objects and comparing objects’ features. The classification and clustering can improve processing time by skipping groups of only one object, and feature comparison on objects in the same group improves accuracy of the detection. YOLO5, the latest version of you only look once (YOLO) developed by Ultralytics LLC, and K-means are applied to classify and group the objects in the first stage. Then, modified Zernike moments (MZMs) and correlation coefficients are used for the features extraction and matching in the second stage. The Open Images V6 data set is used to train the YOLO5 model. The combination of YOLO5 and MZM makes the effectiveness of the proposed method for copy-move image detection with an average accuracy of 94.26% for images of benchmark and MICC-F600 and 95.37% for natural images. The outstanding feature of the method is that it can balance both processing time and accuracy in detecting duplicate regions on the image.

The problem is then solved by doing the following steps: Build a method to detect objects and compare their features to find the similarity if they are copy-move objects; use YOLO5 for the object detection and group the same category objects; ignore the group having only one object and extract the features of the other groups by MZMs; detect copy-move regions using K-means clustering; and calculate and compare the detection accuracy of the proposed method and related methods.

The main contributions of this paper include: Reduce the processing time by using YOLO5 in objects detection and K-means in clustering; improve the accuracy by using MZM to extract features and correlation coefficients to matching them; and implement and prove the effectiveness of the proposed method for three copy-move data sets: benchmark, MICC-F600 and author-built images.

With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.

This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.

Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.

The Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

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.

This paper provides an introduction to research in the field of image forensics and asks whether advances in the field of algorithm development and digital forensics will facilitate the examination of images in the scientific publication process in the near future.

This study looks at the status quo of image analysis in the peer review process and evaluates selected articles from the field of Digital Image and Signal Processing that have addressed the discovery of copy-move, cut-paste and erase-fill manipulations.

The article focuses on forensic research and shows that, despite numerous efforts, there is still no applicable tool for the automated detection of image manipulation. Nonetheless, the status quo for examining images in scientific publications remains visual inspection and will likely remain so for the foreseeable future. This study summarizes aspects that make automated detection of image manipulation difficult from a forensic research perspective.

Results of this study underscore the need for a conceptual reconsideration of the problems involving image manipulation with a view toward the need for interdisciplinary collaboration in conjunction with library and information science (LIS) expertise on information integrity.

This study not only identifies a number of conceptual challenges but also suggests areas of action that the scientific community can address in the future.

Image manipulation is often discussed in isolation as a technical challenge. This study takes a more holistic view of the topic and demonstrates the necessity for a multidisciplinary approach.

Forgery is not an oft‐discussed subject. Detailed discussion of forgery by banks and other financial institutions all over the world is a taboo, ‘for fear of sowing the seeds of fraudulent schemes in other ingenious heads’. This is obvious. For once a loophole is identified and discussed, it has to be permanently plugged and sealed, which the bankers perhaps find an arduous task as it costs money and leads to the necessity of cumbersome and time‐consuming procedures being adopted. This paper will analyse the offence of forgery, the definition and the modus operandi of forgery and a sample of forgery cases in the UK and the USA.

The purpose of this paper is to propose an effective method to perform off‐line signature verification and identification by applying a local shape descriptor pyramid histogram of oriented gradients (PHOGs), which represents local shape of an image by a histogram of edge orientations computed for each image sub‐region, quantized into a number of bins. Each bin in the PHOG histogram represents the number of edges that have orientations within a certain angular range.

Automatic signature verification and identification are then studied in the general binary and multi‐class pattern classification framework, with five different common applied classifiers thoroughly compared.

Simulation experiments show that PHOG has obvious advantages in the extraction of discriminating information from handwriting signature images compared with many previously proposed signature feature extraction approaches. The experiments also demonstrate that several classifiers, including k‐nearest neighbour, multiple layer perceptron and support vector machine (SVM) can all give very satisfactory performance with regard to false acceptance rate (FAR) and false rejection rate (FRR). On a public benchmarking signature database “Grupo de Procesado Digital de Senales” (GPDS), experiments demonstrate an FRR of 4.0 percent and an FAR 3.25 percent from SVM for skillful forgery, which compares sharply with the latest published results of FRR 16.4 percent and FAR 14.2 percent on the same dataset. Experiments on a second DAVAB off‐line signature database also illustrate the superiority of the proposed method. The related issue, off‐line signature recognition, which is to find the identification of the signature owner from a given signature database, is also investigated based on the PHOG features, showing superb classification accuracies of 99 and 96 percent for GPDS and DAVAB datasets, respectively.

The proposed method for off‐line signature verification and recognition has a promising potential of designing a real‐world system for many applications, particularly in forensics and biometrics.

In this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.

These drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.

The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.