Copy Move Research Articles

Deep Learning Methods for Copy Move Image Forgery Detection: A Review

Deep Learning Methods for Copy Move Image Forgery Detection: A Review

Copy Move Forgery detection and localisation robust to rotation using block based Discrete Cosine Transform and eigenvalues

The contemporary era faces a widespread issue with digital image forgery, posing a significant challenge due to its ease and the broad reach enabled by high-speed internet. This manipulation of images carries substantial socio-political implications globally. Hence, robust digital image forensic methods are critical for detecting such forgeries. This article presents an innovative algorithm specifically designed to detect and locate copy-move duplication within digital images. By utilising the Discrete Cosine Transform and eigenvalues as distinguishing features, the algorithm precisely identifies and pinpoints replicated image regions from overlapping pixel blocks. Uniquely, another cumulative DCT feature enhances the algorithm’s ability to discern duplicated regions, even when subjected to post-processing rotation attacks. Experiments using various datasets demonstrate that this method outperforms avant-garde techniques in detecting and localising forgeries, showcasing promising results. This approach contributes significantly to the field of digital image forensics, providing a valuable tool for identifying and localising manipulated content.

Analysis of Copy Move Forgery Detection Process by Applying Fuzzy C Means Algorithm Based on Deep Learning in Digital Image Processing

Analysis of Copy Move Forgery Detection Process by Applying Fuzzy C Means Algorithm Based on Deep Learning in Digital Image Processing

Modified Keypoint-Based Copy Move Area Detection

Detecting and identifying manipulated portions within images poses a formidable challenge in research. Manipulated images, often created using image editing software such as Picasa or Photoshop, serve to obscure information and intentionally mislead viewers. Consequently, ensuring the authenticity of images becomes imperative prior to extracting meaningful data. One prevalent form of tampering is copy-move forgery, where objects are intentionally duplicated within an image using region of the same image. This study introduces a method for detecting copy-move forgery areas based on locating Scale-Invariant Feature Transform (SIFT) keypoints in images. The SIFT technique is employed for feature extraction, while feature descriptors are matched using brute force matching. Subsequently, a clustering algorithm is applied to group spatially proximate keypoints, enabling the detection of cloned regions. Specifically, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is utilized to identify forged areas within the image. To mitigate erroneous forgery detection and reduce false positives, outlier detection techniques are employed. Experimental evaluations are conducted on the MICC-F220 and MICC-F600 datasets, with comparisons drawn against previously established methodologies.

Radial search for accelerating block matching to detect copy-move forgery in digital photographs

One of the exciting areas of image processing research right now is image forensics. The most common kind of image forgery is copy-move (CM) forgery. In this study, a method for quickly detecting CM counterfeiting is proposed. The suggested approach speeds up the block matching process by employing the proposed radial search method. DCT features are extracted from each block and only the most "R' significant features are considered to create the feature vector which in turn shortens the processing time for feature matching. Then radix sorting is used to sort the blocks based on the DCT coefficients, and the proximity of blocks is used to indicate how similar they are. Each pair of adjacent blocks has their difference measured, and if the difference between two blocks is less than a predetermined threshold, the blocks are deemed comparable. Once a match is found, the nearby eight blocks are located using the radial search strategy, which eliminates the need for the traditional sequential block comparison. As opposed to exhaustive block matching, the radial search approach is experimented with which gives commendable improvement in copy move forgery detection.

An approach for copy-move image multiple forgery detection based on an optimized pre-trained deep learning model

Owing to the rapid increase in the availability of editing tools and image capturing, the amount of fake images available online has increased tremendously. Moreover, social media and other networks have become the foremost channel, which is designed for transmitting manipulated images, propagating rumours, spreading fake news, and so on. Thus, developing effective techniques for detecting such forgeries have become highly essential. One of the most widespread kinds of forgery is Copy Move Forgery (CMF), which alters the image by using the patches inside the image. Though deep learning-based techniques offer superior performance, they suffer from generalization issues. Therefore, a transfer learning-based method for identifying CMF utilizing a Deep Convolutional Neural Network (Deep CNN) is proposed to overcome the issue. Deep CNN is trained with the parameters of the pre-trained GoogLeNet, and is employed for detecting multiple forgeries. Furthermore, a novel optimization algorithm, Fractional Leader Harris Hawks Optimization (FLHHO), is created to modify the weights and bias of the Deep CNN. Further, the presented technique is evaluated for its effectiveness on the basis of the parameters like testing accuracy of 0.930, True Negative Rate (TNR) of 0.938, and True Positive Rate (TPR) of 0.941.

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

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

Metaheuristics with Optimal Deep Transfer Learning Based Copy-Move Forgery Detection Technique

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 (ODTL-CMFD) 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, MICC-F2000, 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.

Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection

Modeling of Reptile Search Algorithm With Deep Learning Approach for Copy Move Image Forgery Detection

Retracted: Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1.

[This retracts the article DOI: 10.1155/2022/6845326.].

A Novel Image Copy–Move Forgery Detection Algorithm Using the Characteristics of Local Descriptors

The image copy–move forgery can achieve the realistic effects without leaving any obvious manipulation traces because the copied source and pasted target belong to the homogenous sources. Image copy–move forgery detection (CMFD) is still a serious challenge issue for researchers, e.g. computational efficiency, occlusive (smoothing) object detection, and partial object detection, under various post-processing and geometrical transformations. This paper proposes a novel CMFD scheme using the characteristics of local descriptors. First, the proposed method deeply analyzes the structure and mines the inherent characteristics of the local descriptor (SURF) for feature extraction in the coarse and smooth regions. Subsequently, the proposed method takes the kernel features as coarse feature matching to decrease matching costs. Then, the found candidate keypoints with a relatively small quantity are used with complete features to perform fine keypoint matching for getting suspicious candidate keypoint pairs. Furthermore, based on the deep mining of the inherent local descriptor characteristics, the proposed method can indicate the forgery region localizations through keypoint pairs, even the forgeries suffering from various geometrical transformations. Then, the proposed method proposes adaptive dual-filtering algorithms of [Formula: see text]-means relying on keypoint characteristics, and spatial distance for hierarchical progress of feature filtering. Finally, Delaunay Triangulation relies on the true-positive keypoint pairs to perform the forgery region matting effectively and efficiently. The experimental results demonstrate that the proposed method can achieve the best F1 scores compared to the state-of-the-art methods in most cases, especially in anti-scaling transformations.

A Novel Approach to Detect Copy Move Forgery Using Deep Learning

With the development of readily available image editing tools, manipulating an image has become a universal issue. To check the authenticity, it is necessary to identify how various images might be forged and the way they might be detected using various forgery detection approaches. The importance of detecting copy-move forgery is that it identifies the integrity of an image, which helps in fraud detection at various places such as courtrooms, news reports. This article presents an appropriate technique to detect Copy-Move forgery in which to some extent an image is copied and pasted onto an equivalent image to hide some object or to make duplication. The input image is segmented using the real-time superpixel segmentation algorithm DBSCAN (Density based spatial clustering of application with noise). Due to the high accuracy rate of the VGGNet 16 architecture, it is utilized for feature extraction of segmented images, which will also enhance the efficiency of the overall technique while matching the extracted patches using adaptive patch matching algorithm. The experimental results reveal that the proposed deep learning-based architecture is more accurate in identifying the tempered area even when the images are noisy and can save computational time as compared to existing architectures. For future research, the technique can be enhanced to work on other forgery detection techniques such as image splicing and multi-cloned images.

Copy Move Forgery Detection: The Current Implications and Contemporary Practices

Copy move forgeries are a type of digital retouching that takes place in the field of image modification. This type of retouching occurs when one element of a picture is replicated over to a different segment of the photograph. This is typically done in an effort to mask or cover up undesirable qualities of the photograph thus copy move forgeries are a form of image modification. When it comes to digital picture modalities, copy move forging is by far the most prevalent form of picture manipulation that may take place. This is mainly observed when certain elements or segments of the recorded file are duplicated in the recorded file. The identification and localization of forgeries is a prominent problem that has piqued the attention of scholars working in the field of digital forensics and continues to do so. The identification of photo forgery has previously been the subject of several methodologies and uncountable publications, all of which were created and published. An abridged description of the present methodology and approaches is provided in this document, along with a survey and condensed summaries of current research and successes, among other things.

A Keypoint-Based and Block-Based Fusion Method for Image Copy–Move Forgery Detection

Currently, image copy–move forgery issues have brought increasingly serious problems to the social fields. Many research have been devoted to address the image copy–move forgery issues. However, image copy–move forgery detection (CMFD) is still a challenging problem to image forensics. This paper proposes a coarse-to-fine detection method fusing the superiorities of both keypoint-based and block-based methods. The fusion method gets good geometric invariances of keypoint-based methods and good matching robustness with the invariant moment of block-based methods. In the coarse detection stage, a robust SIFT descriptor is used to extract the candidate keypoints, and then the 2NN test is applied to match the suspicious keypoint couples. The proposed three-pass filtering relying on the Euclidean distance, scaling coefficient ratio, and correlation coefficient of block-based features, remove the false-positive outlier couples. Finally, the scaling coefficient ratio statistics of the remaining keypoint couples get the scaling coefficient ratio between the size of copied/pasted or pasted/copied snippets. In the fine detection stage, the block-based thought relying on the scaling coefficient ratio uses the DAFMT to extract the block-based features of multiple scaling levels. Subsequently, LSH is presented to classify block features and finally indicate the fine forgery snippets. Finally, the morphological operations are presented to indicate the forgery accurately. The benchmark IMD and CoMoFoD image copy–move datasets are used to measure the performances between the proposed fusion method and the state-of-the-art CMFD methods. The numerous experimental results demonstrate that the proposed fusion method achieves nearly the best performances to resist various attacks, especially large-scaling attacks, among the compared state-of-the-art methods.

Effect of Image Splicing and Copy Move Forgery on Haralick Features of a Digital Image

Nowadays, digital images have become the fastest way of transferring information, but the existence of advanced photo-editing tools makes it easy to alter digital image content which may be used as proof in a legal case, thus creating a serious problem. The most frequent techniques of image forgery include splicing and copy move. Splicing is an image forgery technique in which, the forger crops a part of the first image and places it in the second image while Copy move forgery is a type of image manipulation that involves copying and pasting at least one component of an image onto other areas of the same image for the purpose of duplication or removal of objects in the image. Finding the integrity of a digital image is critical since it can be used as a legal proof in a multitude of sectors, including investigation of a crime scene. Equally, finding the features of an image that change as a result of image manipulation such as copy move and splicing is very important as this can be used to distinguish between forged and original image. Therefore, in this paper, we looked upon the effect of image splicing and copy move forgery on haralick features of a digital image. CoMoFoD dataset and Images frames extracted from original and spliced videos were used in thisexperiment. The result of this experiment shows that splicing and copy move manipulations have no effect on haralick features of a digital image. As a result, these features cannot be used to tell if an image has been spliced or was manipulated bya copy move forgery

An optimized technique for copy–move forgery localization using statistical features

Copy–Move Forgery Detection (CMFD) helps to detect copied and pasted areas in one image. It plays a crucial role in legal evidence, forensic investigation, defence, and many more places. In the proposed CMFD method, a two-step identification of forgery is presented. In step one, the suspected image will be classified into either one of two classes that are forged or authentic. Step two is carried out​ only if the suspected is classified as forged, then forged location will be identified using the block-matching procedure. Initially, the suspected image is decomposed into different orientations using Steerable Pyramid Transform (SPT); Grey Level Co-occurrence Matrix (GLCM) features are extracted from each orientation. These features are used to train Optimized Support Vector Machine (OSVM) as well as to classify. If the suspected image is categorized into forged, then the suspected grey image is converted into overlapping blocks, and from each block, GLCM features are extracted. The proper similarity threshold value and distance threshold value can locate the forged region using GLCM block features. The performance of the proposed method is tested using standard datasets CoMoFoD and CASIA Datasets. The proposed CMFD approach results are consistent, even the forged image suffered from attacks like JPEG compression, scaling, and rotation. The OSVM classifier is showing superiority over the Optimized Naive Bayes Classifier (ONBC), Extreme Learning Machine (ELM) and Support Vector Machine (SVM).

Seamless Copy–Move Replication in Digital Images

The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy–move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy–move forgery.

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1.

With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.

Copy Move and Splicing Image Forgery Detection using CNN

The boom of digital images coupled with the development of approachable image manipulation software has made image tampering easier than ever. As a result, there is massive increase in number of forged or falsified images that represent incorrect or false information. Hence, the issue of image forgery has become a major concern and it must be addressed with appropriate solution. Throughout the years, various computer vision and deep learning solutions have emerged with a purpose to detect forgery in case of digital images. This paper presents a novel approach to detect copy move and splicing image forgery using a Convolutional Neural Network (CNN) with three different models i.e. ELA (Error Level Analysis), VGG16 and VGG19. The proposed method applies the pre-processing technique to obtain the images at a particular compression rate. These images are then utilized to train the model and further the images are classified as authentic or forged. The paper also presents the experimental results of the proposed method and performance evaluation in terms of accuracy.

Resunet: Effective Passive Model for Image Copy Move Forgery Localization

Resunet: Effective Passive Model for Image Copy Move Forgery Localization