  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.
