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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]

The digital image proves critical evidence in the fields like forensic investigation, criminal investigation, intelligence systems, medical imaging, insurance claims, and journalism to name a few. Images are an authentic source of information on the internet and social media. But, using easily available software or editing tools such as Photoshop, Corel Paint Shop, PhotoScape, PhotoPlus, GIMP, Pixelmator, etc. images can be altered or utilized maliciously for personal benefits. Various active, passive and other new deep learning technology like GAN approaches have made photo-realistic images difficult to distinguish from real images. Digital image tamper detection now focuses on determining the authenticity and consistency of digital photos. The major research problems use generic solutions and strategies, such as standardized data sets, benchmarks, evaluation criteria and generalized approaches.This paper overviews the evaluation of various image tamper detection methods. A brief discussion of image datasets and a comparative study of image criminological (forensic) methods are included in this paper. Furthermore, recently developed deep learning techniques along with their limitations have also been addressed. This study aims to comprehensively analyze image forgery detection methods using conventional and advanced deep learning approaches. [ABSTRACT FROM AUTHOR]

In the last years, due to the availability and easy of use of image editing tools, a large amount of fake and altered images have been produced and spread through the media and the Web. A lot of different approaches have been proposed in order to assess the authenticity of an image and in some cases to localize the altered (forged) areas. In this paper, we conduct a survey of some of the most recent image forgery detection methods that are specifically designed upon Deep Learning (DL) techniques, focusing on commonly found copy-move and splicing attacks. DeepFake generated content is also addressed insofar as its application is aimed at images, achieving the same effect as splicing. This survey is especially timely because deep learning powered techniques appear to be the most relevant right now, since they give the best overall performances on the available benchmark datasets. We discuss the key-aspects of these methods, while also describing the datasets on which they are trained and validated. We also discuss and compare (where possible) their performance. Building upon this analysis, we conclude by addressing possible future research trends and directions, in both deep learning architectural and evaluation approaches, and dataset building for easy methods comparison. [ABSTRACT FROM AUTHOR]

SDN is a modern internet architecture that has transformed the traditional internet structure in recent years. By segregating the control and data planes of the network, SDN facilitates centralized management, scalability, dynamism, and programmability. However, this very feature makes SDN controllers vulnerable to cyber attacks, which can cause network-wide crashes, unlike conventional networks. One of the most stealthy attacks that SDN controllers face is the relay link forgery attack in topology deception attacks. Such an attack can result in erroneous overall views for SDN controllers, leading to network functionality breakdowns and even crashes. In this article, we introduce the Relay Link Forgery Attack detection model based on the Transformer deep learning model for the first time. The model (RLFAT) detects relay link forgery attacks by extracting features from network flows received by SDN controllers. A dataset of network flows received by SDN controllers from a large number of SDN networks with different topologies was collected. Finally, the Relay-based Link Forgery Attack detection model was trained on this dataset, and its performance was evaluated using accuracy, recall, F1 score, and AUC metrics. For better validation, comparative experiments were conducted with some common deep learning models. The experimental results show that our proposed model (RLFAT) has good performance in detecting RLFA and outperforms other models. [ABSTRACT FROM AUTHOR]

Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images taken under UV light. The results obtained with the proposed algorithm have been compared with the results obtained with state-of-the-art machine learning and deep learning algorithms. It is seen that the results obtained with the proposed algorithm are superior to the results obtained with the state-of-the-art machine learning and deep learning algorithms. The proposed algorithm achieved 100% accuracy on the training set and 99.95% accuracy on the test set while yielding low inference time with relatively few parameters and small file size. [ABSTRACT FROM AUTHOR]

The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment, photographing habits, camera resolution and other factors, resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits. Developing an anticounterfeiting code authentication algorithm based on mobile phones is of great commercial value. Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes, the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy. To address the small differences in texture features, low response speed and excessively large deep learning models used in mobile phone anti-counterfeiting and identification scenarios, we propose a feature-guided double pool attention network (FG-DPANet) to solve the reprinting forgery problem of printing anti-counterfeiting codes. To address the slight differences in texture features in high-quality reprinted anti-counterfeiting codes, we propose a feature guidance algorithm that creatively combines the texture features and the inherent noise feature of the scanner and printer introduced in the reprinting process to identify anti-counterfeiting code authenticity. The introduction of noise features effectively makes up for the small texture difference of high-quality anti-counterfeiting codes. The double pool attention network (DPANet) is a lightweight double pool attention residual network. Under the condition of ensuring detection accuracy, DPANet can simplify the network structure as much as possible, improve the network reasoning speed, and run better on mobile devices with low computing power. We conducted a series of experiments to evaluate the FG-DPANet proposed in this paper. Experimental results show that the proposed FG-DPANet can resist highquality and small-size anti-counterfeiting code reprint forgery. By comparing with the existing algorithm based on texture, it is shown that the proposed method has a higher authentication accuracy. Last but not least, the proposed scheme has been evaluated in the anti-counterfeiting code blurring scene, and the results show that our proposed method can well resist slight blurring of anti-counterfeiting images. [ABSTRACT FROM AUTHOR]

Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER). [ABSTRACT FROM AUTHOR]

Video forgery detection and localization is one of the most important issue due to the advanced editing software that provides strengthen to tools for manipulating the videos. Object based video tampering destroys the originality of the video. The main aim of the video forensic is to eradicate the forgeries from the original video that are useful in various applications. However, the research on detecting and localizing the object based video forgery with advanced techniques still remains the open and challenging issue. Many of the existing techniques have focused only on detecting the forged video under static background that cannot be applicable for detecting the forgery in tampered video. In addition to this, conventional techniques fail to extract the essential features in order to investigate the depth of the video forgery. Hence, this paper brings a novel technique for detecting and localizing the forged video with multiple features. The steps involved in this research are keyframe extraction, pre-processing, feature extraction and finally detection and localization of forged video. Initially, keyframe extraction uses the Gaussian mixture model (GMM) to extract frames from the forged videos. Then, the pre-processing stage is manipulated to convert the RGB frame into a grayscale image. Multi-features need to be extracted from the pre-processed frames to study the nature of the forged videos. In our proposed study, speeded up robust features (SURF), principal compound analysis histogram oriented gradients (PCA-HOG), model based fast digit feature (MBFDF), correlation of adjacent frames (CAF), the prediction residual gradient (PRG) and optical flow gradient (OFG) features are extracted. The dataset used for the proposed approach is collected from REWIND of about 40 forged and 40 authenticated videos. With the help of the DL approach, video forgery can be detected and localized. Thus, this research mainly focuses on detecting and localization of forged video based on the ResNet152V2 model hybrid with the bidirectional gated recurrent unit (Bi-GRU) to attain maximum accuracy and efficiency. The performance of this approach is finally compared with existing approaches in terms of accuracy, precision, F-measure, sensitivity, specificity, false-negative rate (FNR), false discovery rate (FDR), false-positive rate (FPR), Mathew's correlation coefficient (MCC) and negative predictive value (NPV). The proposed approach assures the performance of 96.17% accuracy, 96% precision, 96.14% F-measure, 96.58% sensitivity, 96.5% specificity, 0.034 FNR, 0.04 FDR, 0.034 FPR, 0.92 MCC and 96% NPV, respectively. Along with is, the mean square error (MSE) and peak-to-signal-noise ratio (PSNR) for the GMM model attained about 104 and 27.95, respectively. [ABSTRACT FROM AUTHOR]

There are many techniques for faking videos that can alter the face in a video to look like another person. This type of fake video has caused a number of information security crises. Many deep learning-based detection methods have been developed for these forgery methods. These detection methods require a large amount of training data and thus cannot develop detectors quickly when new forgery methods emerge. In addition, traditional forgery detection refers to a classifier that outputs real or fake versions of the input images. If the detector can output a prediction of the fake area, i.e., a segmentation version of forgery detection, it will be a great help for forensic work. Thus, in this paper, we propose a GAN-based deep learning approach that allows detection of forged regions using a smaller number of training samples. The generator part of the proposed architecture is used to synthesize predicted segmentation which indicates the fakeness of each pixel. To solve the classification problem, a threshold on the percentage of fake pixels is used to decide whether the input image is fake. For detecting fake videos, frames of the video are extracted and it is detected whether they are fake. If the percentage of fake frames is higher than a given threshold, the video is classified as fake. Compared with other papers, the experimental results show that our method has better classification and segmentation. [ABSTRACT FROM AUTHOR]

The advancements in Image editing techniques produce realistic‐looking artificial images with ease. These images can easily circumvent the forensic systems making the authentication process more tedious and difficult. To overcome this problem, we introduce a modern convolutional neural network (CNN) named ForensicNet, inspired by the recent developments in computer vision. The three major contributions of our CNNs are inverted bottleneck, separate downsampling layers, and using depth‐wise convolutions for mixing information in the spatial dimension. The inverted bottlenecks help improve accuracy and reduce network parameters/FLOPs. The separate downsampling layers help converge the network. The normalization layers also help stabilize training whenever the spatial resolution is changed. The depth‐wise convolution is a grouped convolution where the number of groups and channels are the same. The experiments show that ForensicNet outperforms the state‐of‐the‐art methods by a large margin. [ABSTRACT FROM AUTHOR]

With the recent advancement in modern technology, one can easily manipulate a digital image or video using computer software or a mobile application. The purpose of editing visual media could be as simple as to look good before sharing to the social networking site's or can be as malicious as to defame or hurt one's reputation in the real world through such morphed visual imagery. Identity theft is one of the examples where one's identity get stolen by some impersonator who can access the personal and financial information of an innocent person. To avoid such drastic situations, law enforcement authorities must use some automatic tools and techniques to find out whether a person is innocent or the culprit. One major question that arises here is how and what parts of visual imagery can be manipulated or edited. The answer to this question is important to distinguish the authentic images/videos from the doctored multimedia. This survey provides a detailed analysis of image and video manipulation types, popular visual imagery manipulation methods, and state-of-the-art image and video forgery detection techniques. It also surveys different fake image and video datasets used in tampering. The goal is to develop a sense of privacy and security in the research community. Finally, it focuses to motivate researchers to develop generalized methods to capture artificial visual imagery which is capable of detecting any type of manipulation in given visual imagery. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lightweight deep learning network (DRRU-Net) for image-splicing forgery detection. DRRU-Net is an architecture that combines RRU-Net for learning the visual content of images and image acquisition artifacts, and a JPEG artifact learning module for learning compression artifacts in the discrete cosine transform (DCT) domain. The backbone model of a network based on pre-training, such as CAT-Net, a representative network for image forgery detection, has a relatively large number of parameters, resulting in overfitting in a small dataset, which hinders generalization performance. Therefore, in this paper, the learning module is designed to learn the characteristics according to the DCT domain in real time without pre-training. In the experiments, the proposed network architecture and training method of DRRU-Net show that the network parameters are smaller than CAT-Net, the forgery detection performance is better than that of RRU-Net, and the generalization performance for various datasets can be improved. [ABSTRACT FROM AUTHOR]

Photo-response non-uniformity (PRNU), as a class of device fingerprint, plays a key role in the forgery detection/localization for visual media. The state-of-the-art PRNU-based forensics methods generally rely on the multi-scale trace analysis and result fusion, with Markov random field model. However, such handcrafted strategies are difficult to provide satisfactory multi-scale decision, exhibiting a high false-positive rate. Motivated by this, we propose an end-to-end multi-scale decision fusion strategy, where a mapping from multi-scale forgery probabilities to binary decision is achieved by a supervised deep fully connected neural network. As the first time, the deep learning technology is employed in PRNU-based forensics for more flexible and reliable integration of multi-scale information. The benchmark experiments exhibit the state-of-the-art accuracy performance of our method in both pixel-level and image-level, especially for false positives. Additional robustness experiments also demonstrate the benefits of the proposed method in resisting noise and compression attacks. [ABSTRACT FROM AUTHOR]

The tremendous growth of data in social media and other platforms has raised an interesting question of authenticity. It has led to an active research area named Digital Forensics. Especially, the face manipulation has become a major issue in character assassination. The image forgery tools are improving everyday thereby posing a challenge in detection systems. The current detection systems provide deep learning based solutions which do not bring the reliability and also have chance to fail when different forgery tool is developed to synthesise or edit the face image. Therefore, an efficient system is required which gives explainability along with efficacy. In this paper, we propose a novel method to detect the forged faces using Image Quality Assessment(IQA) based features. As far as we know IQA has not been used for detecting AI generated images. Despite the visual appearance being same for original and fake images, most of the discriminative information will be available in the frequency domain of those images. With that intuition we have extracted image quality based features from frequency domain and also spatial domain. The proposed method has achieved the highest accuracy of 99% when different types of experiments were performed on standard datasets. The generalisation and explainability of the proposed model have also been discussed. [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]

SUMMARY: Due to the tremendous growth of video editing software, it has become extremely simple to introduce malicious content by manipulating multimedia data. This may include modification of videos either by adding or deleting selective frames with malicious intentions. Hence, it is essential to find the forged frames of the videos by introducing efficient and reliable video forensic methods. This article presents an automatic intra‐frame video forgery detection strategy based on a hybrid optimization tuned deep‐convolutional neural network (deep‐CNN) classifier. The significance of the proposed method lies in developing the proposed raven‐finch optimization algorithm that tunes the weights of the deep‐CNN to exhibit enhanced detection accuracy. The proposed raven‐finch optimization algorithm combines raven search agents and finches search agents, possessing the benefits of both search agents. The features of the input video frames act as the input to the deep‐CNN classifier that detects forgery. The performance of the proposed raven‐finch‐based deep CNN method is analyzed in terms of the performance indices, such as accuracy, sensitivity, and specificity. It is attained to be 97.56%, 95.48%, and 96.38%, respectively, which shows the superiority of the proposed method for intra‐frame forgery detection. [ABSTRACT FROM AUTHOR]

Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What's more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global–local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods. [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]

As social networking services such as Whatsapp, Facebook, Twitter, and Instagram have grown in popularity over the past two decades, the volume of picture data created throughout the globe has exploded. Images that have been altered or doctored using editing software such as Adobe Photoshop, GIMP, and Paint-3D are a major source of concern in the digital age. As a result, it is essential to verify the validity of suspect images before taking action against people who fabricate them. Copy-move forgery and spliced image fraud are two of the most extensively used picture forgery methods in the field. Recent Deep Learning (DL) algorithms have simplified tasks like categorization, localization, segmentation, and other comparable studies. With the use of Residual Neural Networks (ResNet), copy-move forgery and spliced fraud in photographs may be discovered and classified. Experimental results on benchmark datasets such as CASIA-2, MICC-F2000, and CoMoFoD indicate significant gains over state-of-the-art approaches. Gradient Class activation mappings (Grad-CAM) were applied to find forged regions in tampered photographs, and the suggested approach was also proven to be successful in predicting tampered images. On the CoMoFoD dataset, a classification accuracy of 99.9% was attained, while on the MICC-F 2000 dataset, it was 97%. [ABSTRACT FROM AUTHOR]

Selling counterfeit online has become a serious problem, especially with the advancement of social media and mobile technology. Instead of investigating the products directly, one can only check the images, tags annotated by the sellers on the images, or the price to decide if a seller sells counterfeits. One of the ways to detect counterfeit sellers is to investigate their social graphs, in which counterfeit sellers show different behaviour in network measurements, such as those in centrality and EgoNet. However, social graphs are not easily accessible. They may be kept private by the operators, or there are no connections at all. This article proposes a framework to detect counterfeit sellers using their connection graphs discovered fromtheir shared images. Based on 153 K shared images from Taobao, it is proven that counterfeit sellers have different network behaviours. It is observed that the network measurements follow Beta function well. Those distributions are formulated to detect counterfeit sellers by the proposed framework, which is 60% better than approaches using classification. [ABSTRACT FROM AUTHOR]

The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard to distinguish from the real images and are being used unethically against famous personalities such as politicians, celebrities, and social workers. Hence, we propose a method to detect these deepfake images using a light weighted convolutional neural network (CNN). Our research is conducted with Deep Fake Detection Challenge (DFDC) full and sample datasets, where we compare the performance of our proposed model with various state-of-the-art pretrained models such as VGG-19, Xception and Inception-ResNet-v2. Furthermore, we perform the experiments with various resolutions maintaining 1:1 and 9:16 aspect ratios, which have not been explored for DFDC datasets by any other groups to date. Thus, the proposed model can flexibly accommodate various resolutions and aspect ratios, without being constrained to a specific resolution or aspect ratio for any type of image classification problem. While most of the reported research is limited to sample or preview DFDC datasets only, we have also attempted the testing on full DFDC datasets and presented the results. Contemplating the fact that the detailed results and resource analysis for various scenarios are provided in this research, the proposed deepfake detection method is anticipated to pave new avenues for deepfake detection research, that engages with DFDC datasets. [ABSTRACT FROM AUTHOR]

In light of the cumulative use of digital images in a wide range of apps, as well as the accessibility of image manipulation software, detecting image alteration has become a difficult task. The copy-move technique is the most commonly used sort of picture counterfeiting. A portion of an image is duplicated and manipulated in various ways. Handcrafted qualities are commonly used in the identification of picture forgery and counterfeiting. It has always been a difficulty with earlier photo reproduction detection systems that they would rather only detect a specific type of tampering if they are aware of specific image features. Deep learning is currently being used to detect image alteration, which is a breakthrough. These strategies were even more efficient than prior methods since they were able to extract complex images from them. The purpose of this work is to teach you about deep learning-based picture forgery detection methods, why they function, and how they can be improved upon. In addition, you will learn about publicly available image forgery datasets. [ABSTRACT FROM AUTHOR]

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It has become easy to alter and tamper the video content flawlessly by using easily available editing software in today's era. Thus, the authenticity of media resources is at high risk. Therefore, one must verify the video to detect the originality of that video content. If the originality and authenticity of the video are compromised, it can change the viewers' perception. This work presents an automatic forgery detection tool that can help to identify the frame insertion type forgery and its location in a video. The deep features are a significant feature in recognizing the forgery and abnormal variations in the video in this work. Based on a parallel CNN model, the proposed method extracts deep features. It also calculates the distance of the correlation coefficient from the deep features, which helps to find the disassociation between the adjacent frames to identify video forgery. The VIFFD and SULFA standard datasets are used to validate the proposed method. It shows that the proposed method is beneficial in differentiating original & insertion type forged videos. The total accuracy of 99.96% achieved in frame-level forgery detection. On video-level forgery detection, 86.5% & 92% accuracy has been achieved in VIFFD & SULFA dataset, respectively. This work also helps to find the inserted frame's location, which benefits in regenerating the original video from the forged video. The proposed method is non-invasive, efficient, robust, and has low time complexity making it suitable for real applications. [ABSTRACT FROM AUTHOR]

In this work a model is introduced to improve forgery detection on the basis of superpixel clustering algorithm and enhanced Grey Wolf Optimizer (GWO) based AlexNet. After collecting the images from MICC-F600, MICC-F2000 and GRIP datasets, patch segmentation is accomplished using a superpixel clustering algorithm. Then, feature extraction is performed on the segmented images to extract deep learning features using an enhanced GWO based AlexNet model for better forgery detection. In the enhanced GWO technique, multi-objective functions are used for selecting the optimal hyper-parameters of AlexNet. Based on the obtained features, the adaptive matching algorithm is used for locating the forged regions in the tampered images. Simulation outcome showed that the proposed model is effective under the conditions: salt & pepper noise, Gaussian noise, rotation, blurring and enhancement. The enhanced GWO based AlexNet model attained maximum detection accuracy of 99.66%, 99.75%, and 98.48% on MICC-F600, MICC-F2000 and GRIP datasets. [ABSTRACT FROM AUTHOR]

Image authorship attribution presents many challenges and difficulties which have increased with the capabilities presented by synthetic image generation through different artificial intelligence algorithms available today. The hypothesis in this research considers the possibility of using artificial intelligence as a tool to detect forgeries through the usage of a deep learning algorithm. The proposed algorithm was trained using a dataset comprised of paintings by Rembrandt and other 17th century Dutch painters. Three experiments were performed with the proposed algorithm. The first was to build a classifier able to ascertain whether a painting belongs to the Rembrandt or non-Rembrandt category, depending on whether it was painted by this author or not. The second tests included other 17th century painters in four categories. Artworks could be classified as Rembrandt, Eeckhout, Leveck or other Dutch painters. The third experiment used paintings generated by Dall-e 2 and attempted to classify them using the prior categories. Experiments confirmed the hypothesis with best executions reaching accuracy rates of more than 90%. Future research with extended datasets and improved image resolution are suggested to improve the obtained results. [ABSTRACT FROM AUTHOR]

Image manipulation of the human face is a trending topic of image forgery, which is done by transforming or altering face regions using a set of techniques to accomplish desired outputs. Manipulated face images are spreading on the internet due to the rise of social media, causing various societal threats. It is challenging to detect the manipulated face images effectively because (i) there has been a limited number of manipulated face datasets because most datasets contained images generated by GAN models; (ii) previous studies have mainly extracted handcrafted features and fed them into machine learning algorithms to perform manipulated face detection, which was complicated, error-prone, and laborious; and (iii) previous models failed to prove why their model achieved good performances. In order to address these issues, this study introduces a large face manipulation dataset containing vast variations of manipulated images created and manually validated using various manipulation techniques. The dataset is then used to train a fine-tuned RegNet model to detect manipulated face images robustly and efficiently. Finally, a manipulated region analysis technique is implemented to provide some in-depth insights into the manipulated regions. The experimental results revealed that the RegNet model showed the highest classification accuracy of 89% on the proposed dataset compared to standard deep learning models. [ABSTRACT FROM AUTHOR]

The prime goal of creating synthetic digital data is to generate something very closer to real ones when the original data are scarce. However, the trustworthiness of such digital content is dipping potentially in society owing to malicious users. Deepfake method that uses computer graphics and computer vision techniques to replace the face of one person with the face of a different person is becoming an area of big concern. Such techniques can easily be used to hide the identity of a person. Therefore, a method is needed to verify the originality of such face images/videos. To this end, we design a deep learning model enhanced with visual attention technique to differentiate manipulated videos/images (generated by deepfake methods) from real ones. At first, we extract the face region from video frames and then pass the same through the pre-trained Xception model to obtain the feature maps. Next, with the help of the visual attention mechanism, we mainly try to focus on the deepfake video manipulation leftover artifacts. We evaluate our model on two publicly available datasets, namely FaceForensics++ and Celeb-DF (V2), and our model outperforms many state-of-the-art methods tested on these two datasets. Source code of the proposed method can be found at: https://github.com/tre3x/Deepfake-Video-Forgery-Detection. [ABSTRACT FROM AUTHOR]

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• A deep learning-based method is presented for detecting copy-move image forgery. • The generative adversarial networks are used for image quality enhancement. • A tailored dual-branch CNN architecture is used for forgery detection. • The proposed method is able to detect several forged areas, simultaneously. • The proposed method is able to determine the source and target of the forgery. • The proposed method is robust against various pre-processing/post-processing attacks. One of the prevalent methods of image forgery is copy-move, where one or more regions of an image are duplicated and moved elsewhere in the image. It is usually difficult to detect this type of forgery due to the similarity of the copied and forged areas. Also, forgers perform pre-processing and/or post-processing operations on the manipulated regions to make it even more difficult to detect. In this study, an image quality-independent method based on deep learning approach, termed QDL-CMFD, is presented for detecting this type of forgery. QDL-CMFD utilizes generative adversarial networks for image quality enhancement, and convolutional neural networks (CNN) for forgery detection. A tailored dual-branch CNN architecture is introduced consisting of two subnetworks, namely a manipulation detection subnetwork and a similarity detection subnetwork. Accordingly, unlike most existing methods, QDL-CMFD is able to simultaneous detection of several forged areas, as well as determining the source and target of the forgery. Also, QDL-CMFD is robust against various pre-processing/post-processing attacks. It shows excellent performance for detecting low-quality forged images and small areas. Experiments conducted on the CASIA and CoMoFoD benchmark datasets confirm that QDL-CMFD performs significantly better than the competitors. All the implementation source codes of QDL-CMFD are available at https://github.com/MehradAria/QDL-CMFD. [ABSTRACT FROM AUTHOR]

The object-based video forgery detection aims to expose tampered regions from video sequences without any codec information. However, existing methods mainly focus on manually selected features and models for a specific task, either splicing or copy-move, while the general representation ability of deep learning models and the correlation of different forensic features have not been fully explored. In this letter, we propose a dual-stream framework to jointly discover and integrate effective features for object-based video forgery detection. First, two different types of branches are employed to extract discriminative features. Then, after the dual-stream feature fusion, a Conditional Random Field (CRF) layer is utilized to further refine segmentation results. Finally, we consider temporal consistency by incorporating the video tracking strategy. Depth information is adopted to refine the localization results. Extensive experiments on four datasets show that the proposed method achieves competitive performance against the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the continuous development of deep learning techniques, it is now easy for anyone to swap faces in videos. Researchers find that the abuse of these techniques threatens cyberspace security; thus, face forgery detection is a popular research topic. However, current detection methods do not fully use the semantic features of deepfake videos. Most previous work has only divided the semantic features, the importance of which may be unequal, by experimental experience. To solve this problem, we propose a new framework, which is the multisemantic pathway network (MSPNN) for fake face detection. This method comprehensively captures forged information from the dimensions of microscopic, mesoscopic, and macroscopic features. These three kinds of semantic information are given learnable weights. The artifacts of deepfake images are more difficult to observe in a compressed video. Therefore, preprocessing is proposed to detect low-quality deepfake videos, including multiscale detail enhancement and channel information screening based on the compression principle. Center loss and cross-entropy loss are combined to further reduce intraclass spacing. Experimental results show that MSPNN is superior to contrast methods, especially low-quality deepfake video detection. [ABSTRACT FROM AUTHOR]

Handwritten signature verification is a challenging task because signatures of a writer may be skillfully imitated by a forger. As skilled forgeries are generally difficult to acquire for training, in this paper, we propose a deep learning-based dynamic signature verification framework, SynSig2Vec, to address the skilled forgery attack without training with any skilled forgeries. Specifically, SynSig2Vec consists of a novel learning-by-synthesis method for training and a 1D convolutional neural network model, called Sig2Vec, for signature representation extraction. The learning-by-synthesis method first applies the Sigma Lognormal model to synthesize signatures with different distortion levels for genuine template signatures, and then learns to rank these synthesized samples in a learnable representation space based on average precision optimization. The representation space is achieved by the proposed Sig2Vec model, which is designed to extract fixed-length representations from dynamic signatures of arbitrary lengths. Through this training method, the Sig2Vec model can extract extremely effective signature representations for verification. Our SynSig2Vec framework requires only genuine signatures for training, yet achieves state-of-the-art performance on the largest dynamic signature database to date, DeepSignDB, in both skilled forgery and random forgery scenarios. Source codes of SynSig2Vec will be available at https://github.com/LaiSongxuan/SynSig2Vec. [ABSTRACT FROM AUTHOR]

With the growth of digital media data manipulation in today's era due to the availability of readily handy tampering software, the authenticity of records is at high risk, especially in video. There is a dire need to detect such problem and do the necessary actions. In this work, we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical computations. The proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to these features to find the forgery in the video. This work calculates the correlation coefficient from the deep features of the adjacent frames rather than calculating directly from the frames. We divide the procedure of forgery detection into two phases-video forgery detection and video forgery classification. In video forgery detection, this approach detect input video is original or tampered. If the video is not original, then the video is checked in the next phase, which is video forgery classification. In the video forgery classification, method reviewthe forged video for insertion forgery, deletion forgery, and also again check for originality. The proposedwork is generalized and it is tested on two different datasets. The experimental results of our proposed model show that our approach can detect the forgery with the accuracy of 91% on VIFFD dataset, 90% in TDTV dataset and classify the type of forgery-insertion and deletion with the accuracy of 82% on VIFFD dataset, 86% on TDTV dataset. This work can helps in the analysis of original and tempered video in various domain. [ABSTRACT FROM AUTHOR]

With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of publicly available benchmark datasets for malicious manipulation detection. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena. [ABSTRACT FROM AUTHOR]

Blind image forgery prediction in the field of image forgery is difficult. Hence, it is the major attention for the investigators recently. This work introduces an innovative methodology for blind image forgery detection. The steps are described as: initially, input images are pre-processed using hybrid homomorphic filtering to enhance the images. After pre-processing images, features such as Hybrid Speeded up Robust features and scale-invariant feature transform (hybrid SURF-SIFT) and hybrid wavelet features are extracted. According to the extracted features hybrid deep belief neural network (HDBN) framework performs the block matching to examine the forgery region in images. Here, modified atom search optimization is utilized for weights optimization in HDBN framework and improving the performance of matching process. The HDBN framework detects non matching regions, and finally, the forgery region is accurately localized with the presented Adaptive fuzzy clustering based improved sun flower optimization (AFCSO) approach. The implementation platform used in work is PYTHON. The presented technique is tested with three datasets: CG-1050, SMIFD, and Coverage. Moreover, the experimental results of the presented approach is examined with the existing techniques in regard to accuracy, precision, recall, F-measure, True positive rate, False positive rate, True negative rate, and False-negative rate. The performance measures of forgery detection with CG-1050 dataset is F-measure (98.98%), Accuracy (99%), recall (98%), precision (99.9%), True positive rate (99.5%), False positive rate (0.4%), True negative rate (99.7%), False negative rate (2%). The performance measures of forgery detection with SMIFD dataset is F-measure (99.15%), True positive rate (99.57%), Accuracy (98.67%), precision (99.57%), false-positive rate (1.56%), True negative rate (98.43%), and false-negative rate (1.25%). The performance measures of forgery detection with COVERAGE dataset is F-measure (98%), True positive rate (98%), False positive rate (2%), Accuracy (98.3%), precision (97.1%), True negative rate (98%), False negative rate (2%). This proved that the presented approach outperforms the compared existing approaches. [ABSTRACT FROM AUTHOR]

The easy availability and usability of photo-editing tools have increased the number of forgery attacks, primarily splicing attacks, thereby increasing cybercrimes. Because of an existing image-splicing tamper-detection algorithm based on deep learning with high model complexity and weak robustness, a multiscale fusion lightweight model for image-splicing tamper detection is proposed. For the above problems and to improve MobileNetV2, the structural block of the classification part of the original network structure was removed, the stride of the sixth largest structural block of the network was changed to 1, the dilated convolution was used instead of downsampling, and the features extracted from the second and third large structural blocks in the network were downsampled with maximal pooling; then, the constraint on the backbone network was increased by jumping connections. Combined with the pyramid pooling module, the acquired feature layers were divided into regions of different sizes for average pooling; then, all feature layers were fused. The experimental results show that it had a low number of parameters and required a small amount of computation, achieving 91.0% and 96.4% precision on CASIA and COLUMB, respectively, and 83.2% and 88.1% F-measure on CASIA and COLUMB, respectively. [ABSTRACT FROM AUTHOR]

Certificates are often falsified, such as fake diplomas and forged transcripts. As such, many schools and educational institutions have begun to issue diplomas online. Although diplomas can be issued conveniently anytime, anywhere, there are many cases wherein diplomas are forged through hacking and forgery. This paper deals with the required Blockchain diploma. In addition, we use an automatic translation system, which incorporates natural language processing, to perform verification work that does not require an existing public certificate. The hash algorithm is used to authenticate security. This paper also proposes the use of these security protocols to provide more secure data protection. In addition, each transaction history, whether a diploma is true or not, may be different in length if it is presented in text, but converting it into a hash function means that it is always more than a certain length of SHA-512 or higher. It is then verified using the time stamp values. These chaining codes are designed. This paper also provides the necessary experimental environment. At least 10 nodes are constructed. Blockchain platform development applies and references Blockchain standardization, and a platform test, measurement test, and performance measurement test are conducted to assess the smart contract development and performance measurement. A total of 500 nodes were obtained by averaging 200 times, and a Blockchain-based diploma file was agreed upon at the same time. It shows performance information of about 4100 TPS. In addition, the analysis of artificial intelligence distribution diagram was conducted using a four-point method, and the distribution chart was evenly distributed, confirming the diploma with the highest similarity. The verified values were then analyzed. This paper proposes these natural language processing-based Blockchain algorithms. [ABSTRACT FROM AUTHOR]

Copy-move forgery detection identifies a tampered image by detecting pasted and source regions in the same image. In this paper, we propose a novel two-stage framework specially for copy-move forgery detection. The first stage is a backbone self deep matching network, and the second stage is named as Proposal SuperGlue. In the first stage, atrous convolution and skip matching are incorporated to enrich spatial information and leverage hierarchical features. Spatial attention is built on self-correlation to reinforce the ability to find appearance similar regions. In the second stage, Proposal SuperGlue is proposed to remove false-alarmed regions and remedy incomplete regions. Specifically, a proposal selection strategy is designed to enclose highly suspected regions based on proposal generation and backbone score maps. Then, pairwise matching is conducted among candidate proposals by deep learning based keypoint extraction and matching, i.e., SuperPoint and SuperGlue. Integrated score map generation and refinement methods are designed to integrate results of both stages and obtain optimized results. Our two-stage framework unifies end-to-end deep matching and keypoint matching by obtaining highly suspected proposals, and opens a new gate for deep learning research in copy-move forgery detection. Experiments on publicly available datasets demonstrate the effectiveness of our two-stage framework. [ABSTRACT FROM AUTHOR]

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against some known samples, we proposed a document recapture detection scheme by employing a Siamese network to compare and extract distinct features in a recaptured document image. The proposed algorithm takes advantage of both metric learning and image forensic techniques, and forms triplets by considering some important factors in document authentication, e.g., document types, resolutions, and content in each image patch. After training with our triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushing the recaptured samples apart. In the experiment, we consider practical settings under domain differences, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed scheme consistently outperforms the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents (produced by different manufacturers, devices, and substrates), we have achieved 6.92% APCER (Attack Presentation Classification Error Rate) and 8.51% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5.00% BPCER decision threshold. [ABSTRACT FROM AUTHOR]

This paper proposes a commonality learning strategy for face video forgery detection to improve the generalization. Considering various face forgery methods could leave certain similar forgery traces in videos, we attempt to learn the common forgery features from different forgery databases, so as to achieve better generalization in the detection of unknown forgery methods. Firstly, the Specific Forgery Feature Extractors (SFFExtractors) are trained separately for each of given forgery methods. We utilize the U-net structure and consider the triplet loss, location loss, classification loss, and automatic weighted loss to ensure the detection ability of SFFExtractors on the corresponding forgery methods. Next, the Common Forgery Feature Extractor (CFFExtractor) is trained under the supervision of SFFExtractors to explore the commonality of the forgery traces caused by different forgery methods. The extracted common forgery feature is expected to have a good generalization. The experimental results on FaceForensic++ show that the SFFExtractors outperform many state-of-the-arts in face forgery detection. The generalization performance of the CFFExtractor is verified on FaceForensic++, DFDC, and CelebDF. It is proved that commonality learning can be an effective strategy to improve generalization. [ABSTRACT FROM AUTHOR]

Today, images editing software has greatly evolved, thanks to them that the semantic manipulation of images has become easier. On the other hand, the identification of these modifications becomes a very difficult task because the modified regions are not visually apparent. In this article, a new convolutional neural network method based on an encoder/decoder called Fals-Unet is proposed to locate the manipulated regions. The encoder of our method uses an architecture topologically identical to that of the Resnet50 method; its main goal is the exploitation of spatial maps to analyze the discriminating characteristics between the manipulated and non-manipulated regions. The decoding network learns the mapping from low-resolution feature maps to pixel-wise predictions for localizing the falsified regions. Finally, the predicted binary mask (0: falsify, 1: not falsify) is generated by the final layer (softmax). Experimental results on many public datasets CASIA, NIST'16, COVERAGE, and COMOD show that the proposed CNN-based model outperforms some methods. [ABSTRACT FROM AUTHOR]

In digital manipulation, creating fake images/videos or swapping face images/videos with another person is done by using a deep learning algorithm is termed deepfake. Fake pornography is a harmful one because of the inclusion of fake content in the hoaxes, fake news, and fraud things in the financial. The Deep Learning technique is an effective tool in the detection of deepfake images or videos. With the advancement of Generative adversarial networks (GAN) in the deep learning techniques, deep fake has become an essential one in the social media platform. Fake faces may threaten the public, therefore detection of deepfake images/videos is needed. For detecting the forged images/videos, many research works have been done and those methods are inefficient in the detection of new threats or newly created forgery images or videos, and consumption time is high. Therefore, this paper focused on the detection of different types of fake images or videos using Fuzzy Fisher face with Capsule dual graph (FFF-CDG). The data set used in this work is FFHQ, 100K-Faces DFFD, VGG-Face2, and WildDeepfake. The accuracy for FFHQ data sets, the existing and proposed systems obtained the accuracy of 81.5%, 89.32%, 91.35%, and 95.82%, respectively. [ABSTRACT FROM AUTHOR]

Image recaptured from a high-resolution LED screen or a good quality printer is difficult to distinguish from its original counterpart. The forensic community paid less attention to this type of forgery than to other image alterations such as splicing, copy-move, removal, or image retouching. It is significant to develop secure and automatic techniques to distinguish real and recaptured images without prior knowledge. Image manipulation traces can be hidden using recaptured images. For this reason, being able to detect recapture images becomes a hot research topic for a forensic analyst. The attacker can recapture the manipulated images to fool image forensic system. As far as we know, there is no prior research that has examined the pros and cons of up-to-date image recaptured techniques. The main objective of this survey was to succinctly review the recent outcomes in the field of image recaptured detection and investigated the limitations in existing approaches and datasets. The outcome of this study provides several promising directions for further significant research on image recaptured detection. Finally, some of the challenges in the existing datasets and numerous promising directions on recaptured image detection are proposed to demonstrate how these difficulties might be carried into promising directions for future research. We also discussed the existing image recaptured datasets, their limitations, and dataset collection challenges. [ABSTRACT FROM AUTHOR]

The digital world has been witnessing a fast progress in technology, which led to an enormous increase in using digital devices, such as cell phones, laptops, and digital cameras. Thus, photographs and videos function as the primary sources of legal proof in courtrooms concerning any incident or crime. It has become important to prove the trustworthiness of digital multimedia. Inter-frame video forgery one of common types of video manipulation performed in temporal domain. It deals with inter-frame video forgery detection that involves frame deletion, insertion, duplication, and shuffling. Deep Learning (DL) techniques have been proven effective in analysis and processing of visual media. Dealing with video data needs to handle the third dimension (the time dimension), which means extracting temporal features as well as spatial features. The proposed model is built based on the Three Dimension Convolution Neural Network (3D-CNN). Through preprocessing operation that introduced difference frames that pick up the difference in successive adjacent frames, which provide a large quantity of temporal information and lead to enhance the effectiveness of the proposed model. The model achieves high accuracy of 99%. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The emergence of photo editing applications, like Adobe Photoshop, has manipulated the operation of digital images into a simple task. However, these manipulations of images misrepresent the content of the original image for misleading the public. Various copy move forgery detection techniques are developed, but these show less robustness on the image with noise and blurring. This article develops an optimization‐driven deep learning technique for image forgery detection. The purpose is to develop a copy‐move image forgery detection technique using a deep neuro‐fuzzy network and a newly developed optimization algorithm. Here, adaptive partitioning is adapted using a rectangular search for splitting the image into different parts. In addition, the features like local Gabor XOR pattern and Texton features are extracted from the partition. Furthermore, the forgery is detected using the deep neuro‐fuzzy network. Finally, the deep neuro‐fuzzy network training is performed using the proposed multi‐verse invasive weed optimization (MVIWO) technique. The proposed MVIWO method will be newly designed by integrating the multi‐verse optimizer and invasive weed optimization technique. Thus, the copy‐move image forgery detection is effectively performed using the proposed MVIWO‐based deep neuro‐fuzzy network. The developed MVIWO‐based deep neuro‐fuzzy network offers superior performance with the highest specificity of 93.54%, highest accuracy of 94.01%, and highest sensitivity of 97.75%. [ABSTRACT FROM AUTHOR]