Copy Move Forgery Detection Using Forensic Images

Abstract

Digital images are open to several manipulations and dropped cost of compact cameras and mobile phones due to the robust image editing tools. Image credibility is therefore become doubtful, particularly where photos have power, for instance, news reports and insurance claims in a criminal court. Images forensic methods therefore measure the integrity of image by apply different highly technical methods established in literatures. The present work deals with copy move forgery images of Media Integration and Communication Center Forgery (MICC-F2000) dataset for detecting and revealing the areas that have been tampered portion in the image, the image is sectioned into non overlapping blocks using Simple liner iterative clustering (SLIC) method. Then, Scale invariant feature transform (SIFT) descriptor is applied on the grey of the handled image to gives distinctive key points that classified by K-Nearest neighbor to detect and localize the forged portion in the tempered image. The forgery detection results gave a performance percent of about 98%, which reflects the ability of the KNN classifier that cooperated with SIFT descriptor to detect the forged portions even if the forged area is rotated or scaled or both of them.

Image Splicing Detection Based on Discrete Wavelet Transform and co-occurrence Matrix

“…Due to the cut-and-paste operations, the aforementioned forgery methods introduce many changes in the original image that enables tracking the traces of tampering that would not be noticed by human eyes. Some of these changes are abrupt and sharp edges and corners in addition to light, noise, and statistical inconsistencies in the forged image [2].…”