Keypoint-based passive method for image manipulation detection

Due to the availability of media editing software, the authenticity and reliability of digital images are important. Region manipulation is a simple and effective method for digital image forgeries. Hence, the potential to identify the image manipulation is current research issue these days and copy-move forgery detection (CMFD) is a main domain in image authentication. In copy-move forgery, one region is simply copied and pasted over other regions in the same image for manipulating the image. In this paper, we have proposed a method based on Harris corner and Adaptive non-maximal Suppression (ANMS) for manipulation detection in an image. Initially, the input image is taken and then Harris corner detection algorithm is used to detect the interest points and ANMS is adopted to control the number of Harris points in an image. This gives a proper number of interest points for the different size of images and gives the assurance for finding the manipulated region in manageable time. For each extracted interest points we calculate the descriptors using SIFT then for the matching process of obtained descriptors, we use the outlier rejection with the nearest neighbour. Here, RANSAC is used to find the best set of matches to identify the manipulated regions. Experimental results show the robustness against different transformation and post-processing operations.