Duplication Localization and Segmentation

In this paper, we propose a novel method to automatically detect and segment the duplicate regions within an image. Our method takes three steps: 1) detect and locate the duplicate region pair using the modified Efficient Subwindow Search algorithm (ESS), 2) segment duplicate regions using planar homography constraint, and 3) differentiate the tampered region from the authentic one through analysing their contours. The contribution of our method is three-fold: First, we generalize duplication from traditional pure copy-paste, which involves only translation, to more general cases, which involves planar homography transformation (for example, scale and rotation). Second, as for the simple pure translation cases, the time complexity is reduced from best reported O(PlogP) to O(P), where P is the number of pixels in the image. Third, our method is also capable to detect multiple duplications in one image. Performances of our method are evaluated on the INRIA Annotations for Graz-02 dataset (IG02) and experiment results demonstrate that our method reaches pleasing precision and recall as 93.5% and 82.7%, respectively.