Copy-move Forgery Detection Scheme Research Articles

Copy-move forgery detection using Regional Density Center clustering

Copy-move forgery detection is a common image tampering detection technology. In this paper, a novel copy-move forgery detection scheme is proposed. The proposed scheme is based on Regional Density Center (RDC) clustering and Refined Length Homogeneity Filtering (RLHF) policy. First, to obtain an adequate number of keypoints in smooth or small areas of the image, the proposed scheme employs scale normalization and adjustment of the contrast threshold of the input image. Subsequently, to speed up the feature matching process, a matching algorithm based on gray value grouping is used to match the keypoints. RLHF policy is applied to filter the mismatched pairs. To guarantee a good estimation of the affine transformation, the RDC clustering algorithm is proposed to group the matched pairs. Finally, the correlation coefficients are computed to precisely locate the tampered regions. The proposed copy-move forgery detection scheme based on RDC and RLHF can effectively identify duplicated regions of digital images. It demonstrates the effectiveness and robustness of the proposed scheme over many state-of-the-art schemes on public datasets.

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

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.

Copy-move image forgery detection based on evolving circular domains coverage

The aim of this paper is to improve the accuracy of copy-move forgery detection (CMFD) in image forensics by proposing a novel scheme and the main contribution is evolving circular domains coverage (ECDC) algorithm. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. Firstly, the speed-up robust feature (SURF) in log-polar space and the scale invariant feature transform (SIFT) are extracted from an entire image. Secondly, generalized 2 nearest neighbor (g2NN) is employed to get massive matched pairs. Then, random sample consensus (RANSAC) algorithm is employed to filter out mismatched pairs, thus allowing rough localization of counterfeit areas. To present these forgery areas more accurately, we propose the efficient and accurate ECDC algorithm to present them. This algorithm can find satisfactory threshold areas by extracting block features from jointly evolving circular domains, which are centered on matched pairs. Finally, morphological operation is applied to refine the detected forgery areas. Experimental results indicate that the proposed CMFD scheme can achieve better detection performance under various attacks compared with other state-of-the-art CMFD schemes.

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.

An End-to-End Dense-InceptionNet for Image Copy-Move Forgery Detection

A novel image copy-move forgery detection scheme using a Dense-InceptionNet is proposed in this paper. Dense-InceptionNet is an end-to-end, multi-dimensional dense-feature connection, Deep Neural Network (DNN). It is the first DNN model to autonomously learn the feature correlations and search the possible forgery snippets through the matching clues. The proposed Dense-InceptionNet consists of Pyramid Feature Extractor (PFE), Feature Correlation Matching (FCM), and Hierarchical Post-Processing (HPP) modules. The PFE module is proposed to extract multi-dimensional and multi-scale dense-features. The features of each layer in this extractor module are directly connected to the preceding layers. The FCM module is proposed to learn the high correlations of deep features and obtain three candidate matching maps. Finally, the HPP module which makes use of three matching maps to obtain a combination of cross-entropies is amenable to better training via backpropagation. Experiments demonstrate that the efficiency of the proposed Dense-InceptionNet is much better than the other state-of-the-art methods while achieving the relative best performance against most known attacks.

An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features

The popularity of image editing software has made it increasingly easy to alter the content of images. These alterations threaten the authenticity and integrity of images, causing misjudgments and possibly even affecting social stability. The copy-move technique is one of the most commonly used approaches for manipulating images. As a defense, the image forensics technique has become popular for judging whether a picture has been tampered with via copy-move, splicing, or other forgery techniques. In this paper, a scheme based on accelerated-KAZE (A-KAZE) and speeded-up robust features (SURF) is proposed for image copy-move forgery detection (CMFD). It is difficult for most keypoint-based CMFD methods to obtain sufficient points in smooth regions. To remedy this defect, the response thresholds for the A-KAZE and SURF feature detection stages are set to small values in the proposed method. In addition, a new correlation coefficient map is presented, in which the duplicated regions are demarcated, combining filtering and mathematical morphology operations. Numerous experiments are conducted to demonstrate the effectiveness of the proposed method in searching for duplicated regions and its robustness against distortions and post-processing techniques, such as noise addition, rotation, scaling, image blurring, joint photographic expert group (JPEG) compression, and hybrid image manipulation. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested CMFD methods.

Copy-move forgery detection using combined features and transitive matching

Recently, the research of Internet of Things (IoT) and Multimedia Big Data (MBD) has been growing tremendously. Both IoT and MBD have a lot of multimedia data, which can be tampered easily. Therefore, the research of multimedia forensics is necessary. Copy-move is an important branch of multimedia forensics. In this paper, a novel copy-move forgery detection scheme using combined features and transitive matching is proposed. First, SIFT and LIOP are extracted as combined features from the input image. Second, transitive matching is used to improve the matching relationship. Third, a filtering approach using image segmentation is proposed to filter out false matches. Fourth, affine transformations are estimated between these image patches. Finally, duplicated regions are located based on those affine transformations. The experimental results demonstrate that the proposed scheme can achieve much better detection results on the public database under various attacks.

Copy-move forgery detection based on compact color content descriptor and Delaunay triangle matching

Copy-move (region duplication) is one of the most common types of image forgeries, in which at least one part of an image is copied and pasted onto another area of the same image. The main aims of the copy-move forgery are to overemphasize a concept or conceal objects by duplicating some regions. Keypoint based copy-move forgery detection (CMFD) method extracts image feature points and employs local image features to identify duplicated regions, which exhibits remarkable detection performance with respect to memory requirement, computational cost, and robustness. However, they usually do not work well when the objects are hidden in smooth background areas. Also, the detection and localization accuracy always be lowered because of poor local image feature computation. In this paper, we present a novel approach for the detection and localization of copy-move forgeries, which is based on color invariance SIFER (Scale-invariant feature detector with error resilience) and FQRHFMs (Fast quaternion radial harmonic Fourier moments). Firstly, the original forgery image is segmented into nonoverlapping and nearly uniform superpixel blocks, and the stable keypoints are extracted adaptively from each superpixel block by incorporating the superpixel contents and color invariance SIFER. Secondly, a set of connected Delaunay triangles is constructed using the extracted image keypoints, and suitable local image feature for each Delaunay triangle is computed by using FQRHFMs and gradient entropy. Thirdly, the local image features and coherency sensitive hashing (CSH) are utilized to match quickly the Delaunay triangles. Finally, the falsely matched Delaunay triangles are removed by employing dense linear fitting (DLF), and the duplicated regions are localized using optimized zero mean normalized cross-correlation (ZNCC) measure. We conduct extensive experiments to evaluate the performance of the proposed copy-move forgery detection scheme, in which encouraging results validate the effectiveness of the proposed technique.

A two-stage localization for copy-move forgery detection

A two-stage localization for copy-move forgery detection (CMFD) is proposed in this paper. In the first stage, rough localization, Simple Linear Iterative Clustering (SLIC) is employed to segment the image into meaningful patches (superpixels). Subsequently, the Weber Local Descriptor (WLD) is proposed to calculate and extract the feature from each superpixel. Then, based on an experimental analysis, a matching threshold is employed to obtain the superpixel matches. Finally, Euclidean distance is employed to filter out the weak features of the superpixels and obtain the rough suspected matches. In the second stage, precise localization, circular blocks with different radii are slid over the above rough suspected regions to extract the block features by employing the Discrete Analytic Fourier–Mellin Transform (DAFMT). Then, Locality-Sensitive Hashing (LSH) is employed to match the candidate circular block matches. Post-processing is applied to further filter out the weak matches and obtain the detected regions. Geometric morphological operations are employed to remove the isolated regions and indicate the final detected regions. The comprehensive experimental results demonstrate that the proposed method performs better on public benchmark databases than do other state-of-the-art CMFD schemes.

Multi-scale feature extraction and adaptive matching for copy-move forgery detection

A copy-move forgery detection scheme by using multi-scale feature extraction and adaptive matching is proposed in this paper. First, the host image is segmented into the non-overlapping patches of irregular shape in different scales. Then, Scale Invariant Feature Transform is applied to extract feature points from all patches, to generate the multi-scale features. An Adaptive Patch Matching algorithm is subsequently proposed for finding the matching that indicate the suspicious forged regions in each scale. Finally, the suspicious regions in all scales are merged to generate the detected forgery regions in the proposed Matched Keypoints Merging algorithm. Experimental results show that the proposed scheme performs much better than the existing state-of-the-art copy-move forgery detection algorithms, even under various challenging conditions, including the geometric transforms, such as scaling and rotation, and the common signal processing, such as JPEG compression and noise addition; in addition, the special cases such as the multiple copies and the down-sampling are also evaluated, the results indicate the very good performance of the proposed scheme.

Iterative Copy-Move Forgery Detection Based on a New Interest Point Detector

In this paper, we propose a novel copy-move forgery detection scheme that can accurately localize duplicated regions with a reasonable computational cost. To this end, a new interest point detector is proposed utilizing the advantages of both block-based and traditional keypoint-based methods. The detected keypoints adaptively cover the entire image, even low contrast regions, based on a uniqueness metric. Moreover, a new filtering algorithm is employed, which can effectively prune the falsely matched regions. Considering the new interest point detector, an iterative improvement strategy is proposed. The whole procedure is iterated along with adjusting the keypoints density based on the achieved information. The experimental results demonstrate that the proposed scheme outperforms the state-of-the-art methods using two public benchmark databases.

A Survey on Keypoint Based Copy-paste Forgery Detection Techniques

Copy-Move forgery is the most common image tampering method to create forged images. The images may be forged to conceal or change the meaning of the photographs. Hence, it becomes important to verify the integrity and authenticity of the images. The copy-move forgery detection can be classified under two heads viz., block based and keypoint based. The block based methods use mostly the similar kind of frameworks but differ in applying feature extraction schemes. The block-based methods are good at detecting the forged regions with high accuracy but is having tremendously high computational complexity. In this paper we review the keypoint approach which is an alternative to block-based approach. The keypoint based copy-move forgery detection schemes involve, detecting and describing the local features of the images by using the algorithms like SIFT and SURF.