![]() ![]() The experimental results demonstrate that the proposed technique has been able to detect the forged regions with higher accuracy as compared to many state-of-the-art copy-move forgery detection methods. We have obtained 0.9834 and 0.9093 average F1 scores at pixel-level for GRIP and CoMoFoD datasets respectively. Experiments have been conducted on two standard datasets - GRIP and CoMoFoD. Finally, blocks matched falsely due to the presence of homogeneous color information like sea, field, and sky are removed using a shift vector aided outlier removal method. Next, the feature vectors of all image blocks are sorted lexicographically, and then similar blocks are identified by matching the features from neighboring blocks. Initially, the input image is divided into overlapping blocks, then LTrP features are extracted from each block to form a single feature vector. This paper introduces a new copy-move image forgery detection technique which relies on a texture feature descriptor called Local Tetra Pattern (LTrP) for block level image comparison used to localize tampered region(s). In this type of forgery, a part of an image is copied and then pasted somewhere else in the same image with the intent to hide key features of the image. One of the most prevalent forms of image tampering is the copy-move forgery attack. ![]() Hence the need of forgery detection techniques which show high accuracy in detection arises. These tools become a bane when used for malicious reasons as users can possibly add or remove important features from an image without leaving any obvious marks of tampering. In modern era it has become increasingly easier to manipulate and tamper digital images, one of the primary reasons being the boon of commonplace availability of powerful image editing tools and software. The experimental results demonstrate that the proposed algorithm's overall performance is superior to other solutions for detecting copy-move forgery images. Three copy-move forgery datasets are used to compare the performances of the proposed algorithm with some state-of-the-art algorithms. Subsequently, image matting is achieved by the Delaunay triangulation algorithm so that the marked areas indicate the forgery regions. Third, a proposed novel Two-Stage Filtering algorithm, including the Grid-Based Filter and the Clustering-Based Filter, is applied to filter out most of the false matching keypoint pairs. Second, a keypoint-matching algorithm is used to match similar keypoints as the candidate keypoint pairs. First, the keypoints of the input image are computed using a keypoint extraction algorithm. In this paper, a novel algorithm is proposed for the detection of copy-move forgery. The copy-move forgery technique is commonly used to temper an image by copying some regions of an image and pasting them somewhere in the same image. With the wide application of simple image editing software, forgery images have become severe social problems with extremely damaging effects. ![]()
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