An improved method for copy-move forgery detection in digital forensic

Abstract

Identification of forged images paid sufficiently great notice among the application of image analysis in digital forensic science. Image forgery detection is piled up by the necessity of authenticity and to preserve integrity of the images. And in majority of the cases copy-move image forgery technique is used to alter the digital image, in which a portion of the image is copied and pasted somewhere else in the same image with the purpose to cover an important image characteristic. Here, copy-move image forgery detection scheme is implemented to detect tampered digital images using adaptive overlapped segmentation and feature point matching. It integrates both block-based and keypoint-based forgery detection methods. Adaptive overlapped segmentation algorithm segments the image into different superpixels. Then, the feature points are extracted from each part using SURF algorithm, and these features are matched with one another, if the match exceeded the preset threshold it will indicate the suspected forgery regions and then merged regions are generated. Finally, detected forgery regions are displayed by applying the morphological operation to the merged regions.