Copy-move Forgery Detection Method Research Articles

LBRT: Local-Information-Refined Transformer for Image Copy-Move Forgery Detection.

The current deep learning methods for copy-move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset.

The Optimal Model for Copy-Move Forgery Detection in Medical Images.

Digital devices can easily forge medical images. Copy-move forgery detection (CMFD) in medical image has led to abuses in areas where access to advanced medical devices is unavailable. Forgery of the copy-move image directly affects the doctor's decision. The method discussed here is an optimal method for detecting medical image forgery. The proposed method is based on an evolutionary algorithm that can detect fake blocks well. In the first stage, the image is taken to the signal level with the help of a discrete cosine transform (DCT). It is then ready for segmentation by applying discrete wavelet transform (DWT). The low-low band of DWT, which has the most image properties, is divided into blocks. Each block is searched using the equilibrium optimization algorithm. The blocks are most likely to be selected, and the final image is generated. The proposed method was evaluated based on three criteria of precision, recall, and F1 and obtained 90.07%, 92.34%, and 91.56%, respectively. It is superior to the methods studied on medical images. It concluded that our method for CMFD in the medical images was more accurate.

Robust audio copy-move forgery detection on short forged slices using sliding window

Audio copy-move forgery detection (CMFD), which aims at finding possible forgeries that are derived from the same audio recording, has been one of the most difficult challenges in blind audio forensics. However, the existing works rarely pay attention to the forgery of short slices within voiced segments, which may result in missed detection. In this work, we present a robust audio CMFD method on the basis of sliding window (SW) and constant Q cepstral coefficients (CQCC). Specifically, we first propose two SW strategies to illustrate how to reveal the possible copy-move forgeries within and between voiced segments. Then, we combine the CQCC feature with the Pearson correlation coefficients to measure the similarity between different short slices. Finally, we evaluate the proposed method, named SW-CQCC, against the state-of-the-art approaches to CMFD on real-world read English and Chinese corpora. Experimental results demonstrate that our SW-CQCC exhibits significantly high effectiveness and robustness in detecting short forged slices within voiced segments, which may be helpful to audio forensic examinations.

IMAGE FORGERY DETECTION USING ADAPTIVE OVER-SEGMENTATION AND FEATURE POINT MATCHING USING MATLAB

Digital image forgery has gotten easier to do as computer technology and image processing tools have advanced. The validity of digital images, however, is a significant problem because they are a common source of information. The adaptive over-segmentation and feature point matching approach outlined below is a recommended technique for identifying fake images using a Matlab software method. Block- based and keypoint-based forgery detection techniques are both integrated into the suggested scheme. First, the proposed Adaptive Over-Segmentation algorithm adaptively and non-overlappingly segments the host picture into irregular blocks using image processing methods. Once the feature points have been extracted from each block as block features, the block features are compared to one another to locate the labeled feature points. This process can roughly identify the regions where a forgery is believed to have taken place. To produce the identified forgery regions, it then applies the morphological operation to the merged regions. The experimental findings show that, in comparison to the current state-of-the-art copy-move forgery detection methods, the proposed copy-move forgery detection scheme can produce significantly better detection results even under a variety of difficult conditions. KEYWORDS: Matlab, Adaptive over-segmentation, Colour block feature, Image matching

Image copy-move forgery detection based on dynamic threshold with dense points

Copy-move tampering is one of the most popular tampering techniques at present. The tampered region of the image has good fusion with the original image, which increases the difficulty of detection. After years of research, the current detection method based on key points still has the following problems: 1) Failure to achieve forgery detection of small areas/self-similar areas/smooth areas, 2) Lack of reasonable feature point extraction methods, 3) The various stages of copy-move forgery detection (CMFD) work are relatively independent and lack close connections, 4) A fixed threshold is used as the region of interest similarity metric in the matching and localization stages. The failure to consider tampered images and the diversity of tampered regions leads to the limited detection capability of the algorithm. Considering the actual situation of tampering with the picture, to solve the above problems, we propose a copy-move forgery detection method based on the dynamic threshold. First, we determine the point extraction strategy in each super pixel block according to the size of the simple line interface calculation (SLIC) super pixel block and the Weber local descriptor (WLD) descriptor to ensure the reasonable allocation of feature points and reduce unnecessary points. These key points are then characterized by the scaling, flip and rotation invariants of the fractional general Jacobi-Fourier moments (FJFMs). Then, the matching and mismatch filtering thresholds of each feature point are determined through the WLD and SLIC features, and the SLIC feature is used to replace the distance threshold to improve the detection accuracy of small manufacturing areas. Finally, based on the matching results and SLIC features, an effective positioning method is proposed to improve the speed and accuracy of positioning. Experimental results show that the proposed algorithm is superior to the classic methods in recent years in terms of time and accuracy.

Forgery detection in medical images with distinguished recognition of original and tampered regions using density-based clustering technique

Telemedicine is one of the most significant part of the medical field. It enables transfer of medical images over the internet which offers telediagnosis facility. Due to transfer of medical images from different channels, detection of their authenticity is a crucial research concern. Copy-move forgery is one of the most popular technique for image manipulation. We propose a method for copy-move forgery detection in medical images. Our method apply boundary extraction followed by Laplacian blob detection from the image to identify regions with similar properties. Further, we utilize Good Features To Track (GFTT) and BinBoost techniques for keypoint extraction and descriptor computation, respectively. Similar descriptors are identified using Hamming distance-based nearest neighbor search. Clustering over keypoints is performed using Ant Colony Density-based Clustering (ACDC) technique. We employ Fast Sample Consensus (FSC) technique for selection of correct matches and removal of imprecise keypoints. Further, we use correlation map generation for localization of manipulated regions. Experimental outcomes display that our technique can efficaciously identify tampered medical images even when manipulated images are sustaining various geometrical and post-processing attacks. Proposed technique can also efficaciously distinguish between original and tampered region within forged medical images using Haralick texture features.

SMDAF: A novel keypoint based method for copy‐move forgery detection

Copy–move forgery poses a significant threat to social life and has aroused much attention in recent years. Although many copy-move forgery detection (CMFD) methods have been proposed, the most existing CMFD methods are short of adaptability in detecting images, which leads to the limitation on detection effects. To solve this problem, the paper proposes a novel keypoint-based CMFD method: second-keypoint matching and double adaptive filtering (SMDAF). Motivated by image matching based on keypoint, the second-keypoint matching method is designed to match keypoints extracted from copy–move forgery images, which can be used for both the single-CMFD and the multiple-CMFD. Then, a double adaptive filter (DAF) based on the AdaLAM algorithm and the KANN-DBSCAN clustering algorithm to filter wrong keypoint matches adaptively are proposed, according to the distinct distribution of keypoints in each image. Finally, the forgery regions are presented by finding their convex hulls and padding them. Compared with existing methods, extensive experiments show that the SMDAF method significantly provides more efficiency in detecting images under simulated real-world conditions, has better robustness when facing images with different post-treatment attacks, and is more effective in distinguishing images that look copy–move forged but are real.

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.

Passive Framework of Sparse Region Duplication Detection from Digital Images

Currently, digital images are widely communicated by media using social media applications. The general public captures the digital images for preserving the family and personal memories and to share with their friends and family. Digital images have been used extensively in forensic science to present the digital images as proof in the court and law enforcement agencies, which present a loophole for the culprits to forge the digital image and change the proofs and evidence. Copy-move forgery (CMF) is among the most widely employed image manipulation methods. In this method, the area of the image is duplicated to some other part to modify its content by applying different postprocessing operations on images like blurring, color reduction, and scaling which is a challenging research problem in copy-move forgery detection (CMFD). In this paper, an efficient and effective CMFD method is presented to identify the single and multiple altered areas in an image in the presence of postprocessing operations. The proposed CMFD method divides the image into circular blocks. It computes a rotation-invariant feature vector from each circular block of the image by applying local intensity order pattern (LIOP) features. The computed feature vectors are then compared using Euclidean distance to locate the suspected image’s forged areas. The experimental results of the proposed CMFD method are reported on three standard datasets of the CMF, namely, CoMoFoD, KLTCI, and MICC-F220. The experimental analysis of the proposed CMFD method on these datasets indicates that it produces robust performance (detection accuracies of 97.29% on the CoMoFoD dataset, 98.53% on the KLTCI dataset, and 97.57% on the MICC-F220 dataset) as compared with state-of-the-art CMFD methods in terms of the standard performance evaluation parameters of the CMF.

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).

A comprehensive evaluation procedure for copy-move forgery detection methods: results from a systematic review

A comprehensive evaluation procedure for copy-move forgery detection methods: results from a systematic review

Performance analysis of various copy-move forgery detection methods

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

A Novel Copy-Move Forgery Detection Algorithm via Feature Label Matching and Hierarchical Segmentation Filtering

Recently, some copy-move forgeries have made use of homogeneous region(s) in an image with the large-scaling attack(s) to highlight or cover the target objects, which is easy to implement but difficult to detect. Unfortunately, existing Copy-Move Forgery Detection (CMFD) methods fail to detect such kinds of forgeries because they are incapable of extracting a sufficient number of effective keypoints in the homogeneous region(s), leading to inaccuracy and inefficiency in detection. In this work, a new CMFD scheme is proposed: 1) An improved SIFT structure with inherent scaling invariance is designed to enhance the capability of extracting effective keypoints in the homogeneous region. 2). The enhancement of massive keypoints extraction in the homogeneous region incurs a heavy computational burden in feature matching (Note that this is a common issue in all CMFD methods). For this reason, a new Feature Label Matching (FLM) method is proposed to break down the massive keypoints into different small label groups, each of which contains only a small number of keypoints, for significantly improved matching effectiveness and efficiency. 3) Identifying true keypoints for matching is a critical issue for performance. In our work, the Hierarchical Segmentation Filtering (HSF) algorithm is newly proposed to filter out suspicious outliers, based on the statistics on the coarse-to-fine hierarchical segmentations. 4) Finally, the fusion of the coarse-to-fine hierarchical segmentation maps fills the forgery regions precisely. In our experiments, the proposed scheme achieves excellent detection performance under various attacks, especially for the homogeneous region(s) detection under large-scaling attack(s). Extensive experimental results demonstrate that the proposed scheme achieves the best F1 scores and least computational cost in addressing the geometrical attacks on the IMD dataset (a comprehensive dataset), and CMH datasets (most forgery samples under geometric attacks). Compared to existing state-of-the-art methods, the proposed scheme raises at least 20% and 25% in terms of F1 scores under scaling factors of 50%, and 200% in large-scaling sub-datasets of IMD, respectively.

CF Model: A Coarse-to-Fine Model Based on Two-Level Local Search for Image Copy-Move Forgery Detection

Copy-move forgery is the most predominant forgery technique in the field of digital image forgery. Block-based and interest-based are currently the two mainstream categories for copy-move forgery detection methods. However, block-based algorithm lacks the ability to resist affine transformation attacks, and interest point-based algorithm is limited to accurately locate the tampered region. To tackle these challenges, a coarse-to-fine model (CFM) is proposed. By extracting features, affine transformation matrix and detecting forgery regions, the localization of tampered areas from sparse to precise is realized. Specifically, in order to further exactly extract the forged regions and improve performance of the model, a two-level local search algorithm is designed in the refinement stage. In the first level, the image blocks are used as search units for feature matching, and the second level is to refine the edge of the region at pixel level. The method maintains a good balance between the complexity and effectiveness of forgery detection, and the experimental results show that it has a better detection effect than the traditional interest-based copy and move forgery detection method. In addition, CFM method has high robustness on postprocessing operations, such as scaling, rotation, noise, and JPEG compression.

Copy Move Forgery Detection based on double matching

Copy Move is a technique widespreadly used in digital image tampering, meaning Copy Move Forgery Detection (CMFD) is still a significant research. In this paper, a novel CMFD method is proposed, including double matching process and region localizing process. In double matching process, the first matching is conducted on Delaunay triangles consisting of Local Intensity Order Pattern (LIOP) keypoints, to find the approximate location of suspicious regions. In order to find sufficient keypoint pairs, the existing set of matching triangles is expanded by adding their neighbors iteratively, covering the whole tampered regions, and the second matching with a looser threshold is conducted on the vertices. In the region localizing process, considering the case of multiple copies, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to classify the keypoint pairs described in a new model. Experimental results indicate that the proposed method, with good robustness, outperforms some state-of-the-art methods.

Improved copy move forgery detection method via L*a*b* color space and enhanced localization technique

The wide availability of easy-to-use image editors has made the authenticity of images questionable. Copy-move is one of the most applied forgery types. A new copy-move forgery detection and localization technique independent from the characteristics of the forged regions is proposed in this paper. SIFT keypoints are obtained from CLAHE applied sub-images extracted from the input image by using RGB and L*a*b* color-spaces. Keypoint matching is realized on the sub-images and duplicated regions are determined roughly to create roughly marked image R. RANSAC is also applied in this stage and generated homography matrix is used to construct transformed roughly marked image R′. The method extracts DCT based features from R and R′ to localize exact borders of the tampered regions on the roughly determined areas by using a dynamic threshold. The proposed method has a new suggestion to determine the threshold dynamically. Tamper localization procedure also utilizes from morphological operations (chosen depending on the characteristic of the image) and Connected Component Labeling to determine exact forge boundaries. Results indicate that the proposed method has a better performance compared with state-of-the-art copy-move forgery detection methods on the GRIP dataset. Scaling attack performance of the method is especially better than similar works as shown in the results.

Multiple Copy-Move Forgery Detection Based on Density Clustering

We propose a new copy-move forgery detection method, which can solve the problems of multiple copy-move forgery, low accuracy and inaccurate tampered region location. First, keypoints and corresponding features of the image are extracted by using AKAZE (accelerated KAZE). Second, features are matched by using the Hamming distance and g2NN which can detect the multiple copy-move forgery. Then, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to cluster the keypoints and remove false matching. Finally, PSNR (peak signal-to-noise ratio) and morphological processing is used to locate tampered region accurately. Experimental results show that the proposed method performs well on geometric transformation, post-processing, multiple copy-move forgery, and tampered region localization.

An intelligent block matching approach for localisation of copy-move forgery in digital images

Block-based copy-move forgery detection (CMFD) methods work with features from overlapping blocks. As overlapping blocks are involved, thresholds related to similarity and the physical distances are defined to identify the duplicated regions. However, these thresholds are controlled manually in localising the forged regions. In order to overcome this, an intelligent block matching approach for localisation is proposed using colour and texture features (CTF) through firefly algorithm. Investigation of the proposed CTF method is carried out on a standard database, which achieved an average true detection rate of 0.98 and an average false detection rate of 0.07. The proposed CTF method is robust against brightness change, colour reduction, blurring, contrast adjustment attacks, and additive white Gaussian noise. Performance analysis of the CTF method validates its superiority over other existing methods.

Robust Detection of Copy-Move Forgery Based on Wavelet Decomposition and Firefly Algorithm

Abstract Nowadays, the whole world is covered with the revolution of digital information so that digital data are easy to transfer, access and process. Modern image processing tools are used to create forged images; by using these tools, forgers introduced forged region in the original image to spread the rumor in the public. Now there is a challenge for the forensic department to prove the authenticity of the original image. So there is required a new efficient copy-move tamper detection method. To fulfill this requirement, a new computational efficient method for copy-move forgery detection using firefly algorithm is proposed.

An intelligent block matching approach for localisation of copy-move forgery in digital images

Block-based copy-move forgery detection (CMFD) methods work with features from overlapping blocks. As overlapping blocks are involved, thresholds related to similarity and the physical distances are defined to identify the duplicated regions. However, these thresholds are controlled manually in localising the forged regions. In order to overcome this, an intelligent block matching approach for localisation is proposed using colour and texture features (CTF) through firefly algorithm. Investigation of the proposed CTF method is carried out on a standard database, which achieved an average true detection rate of 0.98 and an average false detection rate of 0.07. The proposed CTF method is robust against brightness change, colour reduction, blurring, contrast adjustment attacks, and additive white Gaussian noise. Performance analysis of the CTF method validates its superiority over other existing methods.