Region duplication detection based on hybrid feature and evaluative clustering

Digital image are easy to be tampered by the photo editing software. Therefore, digital image forensics which aims at validating the authenticity of the digital image are received wide public concern. Region duplication is a commonly used operation in digital image forgeries. The main aims of the region duplication are to overemphasize or conceal some contents by duplicating some regions on the image. Most of the region duplication methods can be categorized into two main classes:block-based and keypoint-based methods. In this paper, a novel region duplication detection scheme is proposed based on hybrid feature and evaluative clustering. The proposed scheme is divided into two stages: the rough matching and the exact matching. In the rough matching, first, hybrid keypoints are extracted from the input image, and those keypoints are described by the unified descriptors. Second, those keypoints are matched by the g2NN strategy. Third, those matched keypoints are grouped by the proposed clustering based on evaluation. Fourth, affine transformations are estimated between these groups, and Bag of Word is used to filter inaccuracy affine transformations to improve the results of pixel level. When no affine transformation is obtained, in the exact matching, each suspicious region is handled separately. Experimental results indicate that the proposed scheme outperforms the state-of-the-art methods under various conditions.