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Network development has made it easier to access multimedia material and to change it by allowing the collection, modification, and transmission of digital data. Additionally, this has led to a rise in malicious use, including unauthorized data distribution and copying. As the quantity of evil activities increases, security issues such as unauthorized use and image forgery are rising. While security solutions for JPEG-1 images are widely available, there remains a significant gap in protection for JPEG 2000 images. In this paper, we propose a new watermark-based forgery detection method to comply with the JPEG Privacy and Security standards and to authenticate JPEG 2000 image integrity in the discrete wavelet transform (DWT) domain. The method proposed divides JPEG 2000 images into groups of blocks (GOBs) within the DWT domain. The watermark is generated by collaborating with the neighboring GOBs and is embedded in the GOBs. It enables you to respond to the collage attack. Experimental results using various sample images show the superiority of the proposed method, showing negligible visual differences between the original and watermarked JPEG 2000 images. [ABSTRACT FROM AUTHOR]

Image forgery detection is essential for forensic and security applications. In local locations where key points (KPs) are few, traditional approaches that rely on them suffer from restrictions. To tackle this problem, this paper suggests an innovative strategy that combines pre-processing, feature extraction, and classification methods. We specifically present EFP-AEHO, or Enhanced Flower Pollination-Adaptive Elephant Herd Optimization, for modifying the Deep Belief Network's (DBN) weights. Our method provides better performance by first converting RGB images into the Lab color space, then applying Eigenvalue Asymmetry (EAS) to extract texture features. Its efficacy is highlighted by experimental findings on the F2000 dataset, which show testing accuracy of 99.4%, TPR of 98%, and TNR of 99.12%. [ABSTRACT FROM AUTHOR]

The detection of copy-move forgeries has been of utmost relevance in the field of digital image forensics because of the explosive growth of image altering tools. The paper provides a thorough overview of current developments in copy-move forgery detection methods. Block-based, keypoints-based, and deep learning-based methods represent the three distinct categories into which the methodologies in the survey are divided. The papers in each category are thoroughly analysed, taking into consideration important factors including pre-processing techniques, feature extraction strategies, feature matching methods, and performance evaluation using various metrics and datasets. This survey study provides a thorough overview of the state of the field by methodically synthesizing and assessing the surveyed papers, and it also offers helpful insights for researchers and practitioners working to improve the accuracy and robustness of copy–move forgery detection methods in digital image forensics. [ABSTRACT FROM AUTHOR]

Facial biometrics are widely used to reliably and conveniently recognize people in photos, in videos, or from real-time webcam streams. It is therefore of fundamental importance to detect synthetic faces in images in order to reduce the vulnerability of biometrics-based security systems. Furthermore, manipulated images of faces can be intentionally shared on social media to spread fake news related to the targeted individual. This paper shows how fake face recognition models may mainly rely on the information contained in the background when dealing with generated faces, thus reducing their effectiveness. Specifically, a classifier is trained to separate fake images from real ones, using their representation in a latent space. Subsequently, the faces are segmented and the background removed, and the detection procedure is performed again, observing a significant drop in classification accuracy. Finally, an explainability tool (SHAP) is used to highlight the salient areas of the image, showing that the background and face contours crucially influence the classifier decision. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Splice detection models are the need of the hour since splice manipulations can be used to mislead, spread rumors and create disharmony in society. However, there is a severe lack of image-splicing datasets, which restricts the capabilities of deep learning models to extract discriminative features without overfitting. This manuscript presents twofold contributions toward splice detection. Firstly, a novel splice detection dataset is proposed having two variants. The two variants include spliced samples generated from code and through manual editing. Spliced images in both variants have corresponding binary masks to aid localization approaches. Secondly, a novel spatio-compression lightweight splice detection framework is proposed for accurate splice detection with minimum computational cost. The proposed dual-branch framework extracts discriminative spatial features from a lightweight spatial branch. It uses original resolution compression data to extract double compression artifacts from the second branch, thereby making it 'information preserving.' Several CNNs are tested in combination with the proposed framework on a composite dataset of images from the proposed dataset and the CASIA v2.0 dataset. The best model accuracy of 0.9382 is achieved and compared with similar state-of-the-art methods, demonstrating the superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

In the domain of general image forgery detection, a myriad of different classification solutions have been developed to distinguish a "tampered" image from a "pristine" image. In this work, we aim to develop a new method to tackle the problem of binary image forgery detection. Our approach builds upon the extensive training that state-of-the-art image classification models have undergone on regular images from the ImageNet dataset, and transfers that knowledge to the image forgery detection space. By leveraging transfer learning and fine tuning, we can fit state-of-the-art image classification models to the forgery detection task. We train the models on a diverse and evenly distributed image forgery dataset. With five models—EfficientNetB0, VGG16, Xception, ResNet50V2, and NASNet-Large—we transferred and adapted pre-trained knowledge from ImageNet to the forgery detection task. Each model was fitted, fine-tuned, and evaluated according to a set of performance metrics. Our evaluation demonstrated the efficacy of large-scale image classification models—paired with transfer learning and fine tuning—at detecting image forgeries. When pitted against a previously unseen dataset, the best-performing model of EfficientNetB0 could achieve an accuracy rate of nearly 89.7%. [ABSTRACT FROM AUTHOR]

Multimedia forensics is an essential field of research that deals with the authenticity and integrity of multimedia content in the digital world. With the increasing use of digital platforms and social media, the risk of manipulated and misleading digital content has significantly increased, making it crucial to verify and analyze the authenticity of multimedia content presented as evidence. Multimedia manipulation can affect various forms of media, such as audio, video, and images, and poses significant challenges for investigators, including different formats, time complexity, and a vast amount of data. This paper provides an overview of current techniques for multimedia manipulation detection, focusing on image, video, and audio analysis. The paper presents an in-depth analysis of various techniques used in multimedia forensics, including source device identification, tampering detection, and authentication. Furthermore, the paper discusses the limitations of current techniques and highlights future research directions in the field. The review concludes that multimedia forensics is a challenging and constantly evolving field that requires ongoing research to address emerging threats to multimedia content authenticity. The proposed techniques have shown promising results in detecting and analyzing multimedia manipulation, but more research is needed to enhance their accuracy and applicability in real-world scenarios. [ABSTRACT FROM AUTHOR]

Copy-Move Forgery (CMF) is a common form of image manipulation attack that involves copying and pasting a part of an image to another position within the same image. This study proposes a Deep Learning (DL) model for detecting CMF, particularly in the presence of various malicious attacks. The proposed approach involves several steps, including converting the input image to grayscale, preprocessing the image using the Simple Linear Iterative Clustering (SLIC) algorithm to generate superpixel partitions, and then extracting keypoint features using the Speeded Up Robust Features (SURF) detector. Finally, a Generative Adversarial Network (GAN) is employed for feature description and matching. To assess the effectiveness of the approach, the types of features used for copy-move forgery were addressed. The proposed approach was examined under rotation, blurring, jpg compression, and scaling attacks. Furthermore, experimental results showed that the proposed approach can detect multiple CMFs with high accuracy. Finally, the proposed method was compared with recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Network development has made it easier to access multimedia material and to change it by allowing the collection, modification, and transmission of digital data. Additionally, this has led to a rise in malicious use, including unauthorized data distribution and copying. As the quantity of evil activities increases, security issues such as unauthorized use and image forgery are rising. While security solutions for JPEG-1 images are widely available, there remains a significant gap in protection for JPEG 2000 images. In this paper, we propose a new watermark-based forgery detection method to comply with the JPEG Privacy and Security standards and to authenticate JPEG 2000 image integrity in the discrete wavelet transform (DWT) domain. The method proposed divides JPEG 2000 images into groups of blocks (GOBs) within the DWT domain. The watermark is generated by collaborating with the neighboring GOBs and is embedded in the GOBs. It enables you to respond to the collage attack. Experimental results using various sample images show the superiority of the proposed method, showing negligible visual differences between the original and watermarked JPEG 2000 images.

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]

Currently, digital images are easily manipulated using different image editing tools, and they are difficult to detect. Forgery detection is a hot research topic in security and forensics applications. Several computational methods have been employed to detect blind image forgeries, but efficiency is reduced due to drawbacks such as the lack of appropriate techniques and reduced accuracy. Therefore, this work presents an optimal blind forgery detection using deep learning approaches. Initially, the input forgery images are pre-processed using wiener filtering-contrast limited enhanced histogram equalization (WE-CLAHE) approach. Effective features are extracted from the clustered data using Hybrid dual-tree complex wavelet trigonometric transform (Hybrid DTT) and VGGNet. The feature dimensionality is reduced through improved horse herd optimization (IHH). Finally, a hybrid deep convolutional capsule auto encoder (Hybrid DCCAE) framework is presented for optimal image forgery recognition. The adaptive density-based fuzzy clustering (ADFC) approach is presented to segment similar pixel regions. The accuracy of CASIA V1 dataset is 99.23%, the coverage dataset 98.75% accuracy, and the GRIP dataset is 98.07% accuracy. The deep learning based proposed approach enhances the performance of forgery detection better than the existing approaches. [ABSTRACT FROM AUTHOR]

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

Digital images are widely used for informal information sharing, but the rise of fake photos spreading misinformation has raised concerns. To address this challenge, image forensics is employed to verify the authenticity and trustworthiness of these images. In this paper, an efficient scheme for detecting commonly used image smoothing operators is presented while maintaining symmetry. A new lightweight deep-learning network is proposed, which is trained with three different optimizers to avoid downsizing to retain critical information. Features are extracted from the activation function of the global average pooling layer in three trained deep networks. These extracted features are then used to train a classification model with an SVM classifier, resulting in significant performance improvements. The proposed scheme is applied to identify averaging, Gaussian, and median filtering with various kernel sizes in small-size images. Experimental analysis is conducted on both uncompressed and JPEG-compressed images, showing superior performance compared to existing methods. Notably, there are substantial improvements in detection accuracy, particularly by 6.50% and 8.20% for 32 × 32 and 64 × 64 images when subjected to JPEG compression at a quality factor of 70. [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]

Now-a-days biometric systems are useful in recognizing person’s identity but criminals change their appearance in behaviour and psychological to deceive recognition system. To overcome from this problem we are using new technique called Deep Texture Features extraction from images and then building train machine learning model using CNN (Convolution Neural Networks) algorithm. This technique refers as LBPNet or NLBPNet as this technique heavily dependent on features extraction using LBP (Local Binary Pattern) algorithm. In this project we are designing LBP Based machine learning Convolution Neural Network called LBPNET to detect fake face images. Here first we will extract LBP from images and then train LBP descriptor images with Convolution Neural Network to generate training model. Whenever we upload new test image then that test image will be applied on training model to detect whether test image contains fake image or non-fake image. Below we can see some details on LBP. Local binary patterns (LBP) is a type of visual descriptor used for classification in computer vision and is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Due to its discriminative power and computational simplicity, LBP texture operator has become a popular approach in various applications. It can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its robustness to monotonic gray-scale changes caused, for example, by illumination variations. Another important property is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. [ABSTRACT FROM AUTHOR]

In this paper a method to determine whether an image is forged (spliced) or not is presented. The proposed method is based on a classification model to determine the authenticity of a tested image. Image splicing causes many sharp edges (high frequencies) and discontinuities to appear in the spliced image. Capturing these high frequencies in the wavelet domain rather than in the spatial domain is investigated in this paper. Correlation between high-frequency sub-bands coefficients of Discrete Wavelet Transform (DWT) is also described using co-occurrence matrix. This matrix was an input feature vector to a classifier. The best accuracy of 92.79% and 94.56% on Casia v1.0 and Casia v2.0 datasets respectively was achieved. This performance is reached when Cb and Cr channels in YCbCr color space only are utilized to form this feature vector. The outcomes of the experiments showed that the proposed method achieved very good results in comparison with existing splicing detection approaches. [ABSTRACT FROM AUTHOR]

In different scientific and security or surveillance applications, the identification of content-altering manipulation from a video or an image has emerged as a field of increasing interest. Enormous traditional methodologies have been developed over time for detecting image forgeries. Nevertheless, most image forgery methodologies, which exist in the literature are constrained to detecting a specific sort of forgery (either image splicing or else copy-move). Thus, a scheme capable of efficiently as well as accurately detecting the availability of unseen forgeries in an image is essential. A Logit Normalization and Distance Intersection over Union-centered fast regional convolutional neural network (LogDIoU-Faster RCNN) technique is created by the work aimed at IF along with region detection. Concurrently, the region localization and also forgery detection process is executed. Utilizing Dual Band Pass Filter Based Steerable Pyramid Transform (DBPF-SPT), the image is initially decomposed by the framework into numerous sub-bands. For recognizing the forgery along the disparate scales, frequencies, along with orientations, decomposition is used. After that, utilizing Normalized Extended Local Ternary Pattern-Principal Component Analysis (NELTP-PCA), the essential key points are extracted together with chosen from the sub-bands that preserve the image's informative features and also reduce computational complexity. The image is finally identified by LogDIoU- Faster RCNN as a forgery image or not, and also the forgery's location is localized. Superior detection accuracy concerning both categorizing the forgery and also identifying the region is attained by the framework. It stays robust when analogized to the existent top-notch methods as exhibited by the experimental outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology 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.)

Protecting data against tampering is a significant concern in modern times. Digital photographs are essential for displaying information. Digital picture forgeries include adding unusual patterns to real pictures, causing visual heterogeneity. CMF is a sort of digital image forgery in which a segment of an image is linked to a similar picture to cover or recreate forgeried components. The forgery seems authentic since the goal area has the same qualities as the original. Despite several ways to detect CMFD, there exist research gaps such as false detection, excessive execution time and low accuracy. Therefore, to address this issue, we present a hybrid optimization technique and classifier for CMFD. Proposed a novel deep learning technique stacked sparse denoising autoencoder (SSDAE) to classify the images as legitimate or fake. Additionally, the weight and bias parameters of the SSDAE model are optimized using the Grasshopper Optimization Algorithm (GOA) and the Spotted Hyena optimizer (SHO). The experiments are conducted on MICC-F220, MICC-F600, MICC-F2000 and CASIA2.0 datasets. Experimental results indicate that the proposed scheme find out image forgery region with Accuracy = 97.45%; Precision = 98.75%; Recall = 98.25% and F1 = 98.55% on MICC-F200 dataset, Accuracy = 98.92%; Precision = 88.45%; Recall = 85.21% and F1 = 91.41% on MICC-F600 dataset, Accuracy = 99.12%; Precision = 99.25%; Recall = 91.14% and F1 = 85.32% on MICC-F2000 dataset and Accuracy = 98.02%; Precision = 96.03%; Recall = 97.74% and F1 = 97.48% on CASIA 2.0 dataset. [Extracted from the article]

Image forgery detection is a new challenge. One type of image forgery is a copy-move forgery. In this method, part of the image is copied and placed at the most similar point. Given the existing algorithms and processing software, identifying forgery areas is difficult and has created challenges in various applications. The proposed method based on the Equilibrium Optimization Algorithm (EOA) helps image forgery detection by finding forgery areas. The proposed method includes feature detection, image segmentation, and detection of forgery areas using the EOA algorithm. In the first step, the image converts to a grayscale. Then, with the help of a discrete cosine transform (DCT) algorithm, it is taken to the signal domain. With the help of discrete wavelet transform (DWT), its appropriate properties are introduced. In the next step, the image is divided into blocks of equal size. Then the similarity search is performed with the help of an equilibrium optimization algorithm and a suitable proportion function. Copy-move forgery detection using the Equilibrium Optimization Algorithm (CMFDEOA) can find areas of forgery with an accuracy of about 86.21% for the IMD data set and about 83.98% for the MICC-F600 data set. [ABSTRACT FROM AUTHOR]

Image forgery is a crucial part of the transmission of misinfor)mation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research. In this study, a novel image texture feature extraction model based on the generalized k-symbol Whittaker function (GKSWF) is proposed for better image forgery detection. The proposed method is divided into two stages. The first stage involves feature extraction using the proposed GKSWF model, followed by classification using the “support vector machine” (SVM) to distinguish between authentic and manipulated images. Each extracted feature from an input image is saved in the features database for use in image splicing detection. The proposed GKSWF as a feature extraction model is intended to extract clues of tam)pering texture details based on the probability of image pixel. When tested on publicly available image dataset “CASIA” v2.0 (Chinese Academy of Sciences, Institute of Automation), the proposed model had a 98.60% accuracy rate on the YCbCr (luminance (Y), chroma blue (Cb) and chroma red (Cr)) color spaces in image block size of 8 × 8 pixels. The proposed image authentication model shows great accuracy with a relatively modest dimension feature size, supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms. [ABSTRACT FROM AUTHOR]

Copy-move (CM) forgery is a common type of image manipulation that involves copying and pasting a region within an image to conceal or duplicate content. Detection of such forgeries acts as an important part of digital image forensics. Deep learning techniques, such as convolutional neural networks (CNNs), are employed to extract informative features from images. CNNs are known for their ability to capture complex patterns and structures, making them well-suited for image-related tasks like forgery detection. This paper introduces a reptile search algorithm with a deep transfer learning-based CM forgery detection (RSADTL-CMFD) approach. The presented model uses Neural Architectural Search Network (NASNet) for feature extraction in forgery detection which allows the network to effectively capture relevant and discriminative features from the input images. To enhance the performance of the NASNet model, we employ the reptile search algorithm (RSA) for hyperparameter tuning. This algorithm optimizes the network’s hyperparameters, enabling the model to quickly adapt to different forgery detection tasks and achieve superior performance. Finally, extreme gradient boosting (XGBoost) effectively utilizes the extracted features from the deep learning network to classify regions within the image as genuine or manipulated/forged. The experimental result analysis of the RSADTL-CMFD model is tested using benchmark datasets. An extensive comparative study highlighted the enhanced outcomes of the RSADTL-CMFD method over recent techniques.

In the last decades, deep learning (DL) has emerged as a powerful and dominant technique for solving challenging problems in various fields. Likewise, in the field of digital image forensics, a large and growing body of literature investigates DL-based techniques for detecting and classifying tampered regions in images. This article aims to provides a comprehensive survey of state-of-the-art DL-based methods for image-forgery detection. Copy-move images and spliced images, two of the most popular types of forged images, were considered. Recently, owing to advances in DL, DL-based approaches have yielded much better results as compared to traditional non-DL-based ones. The surveyed techniques were proposed by developing or fusing various efficient DL methods, such as CNN, RCNN, or LSTM to adapt to detecting tampered traces.