Improved DSIFT Descriptor Based Copy-Rotate-Move Forgery Detection

In recent years, there has been a dramatic increase in the number of images captured by users. This is due to the wide availability of digital cameras and mobile phones which are able to capture and transmit images. Simultaneously, image-editing applications have become more usable, and a casual user can easily improve the quality of an image or change its content. The most common type of image modification is cloning, or copy-move forgery (CMF), which is easy to implement and difficult to detect. In most cases, it is hard to detect CMF with the naked eye and many possible manipulations (attacks) can be used to make the doctored image more realistic. In CMF, the forger copies part(s) of the image and pastes them back into the same image. One possible transformation is rotation, where an object is copied, rotated and pasted. Rotation-invariant features need to be used to detect Copy-Rotate-Move (CRM) forgery. In this paper we present three contributions. First, a new technique to detec...

• Type: Journal
• Year: 2015
• Where: PSIVT
• Authors: Ali Retha Hasoon Khayeat, Xianfang Sun, Paul L. Rosin
• Keywords: Multimedia, PSIVT 2015