Copy-move forgery detection using image blobs and BRISK feature

One of the most frequently used types of digital image forgery is copying one area in the image and pasting it into another area of the same image; this is known as the copy-move forgery. To overcome the limitations of the existing Block-based and Keypoint-based copy-move forgery detection methods, in this paper, we present an effective technique for copy-move forgery detection that utilizes the image blobs and keypoints. The proposed method is based on the image blobs and Binary Robust Invariant Scalable Keypoints (BRISK) feature. It involves the following stages: the regions of interest called image blobs and BRISK feature are found in the image being analyzed; BRISK keypoints that are located within the same blob are identified; finally, the matching process is performed between BRISK keypoints that are located in different blobs to find similar keypoints for copy-move regions. The proposed method is implemented and evaluated on the copy-move forgery standard datasets MICC-F8multi, MICC-F220, and CoMoFoD. The experimental results show that the proposed method is effective for geometric transformation, such as scaling and rotation, and shows robustness to post-processing operation, such as noise addition, blurring, and jpeg compression.