A copy-move forgery detection method based on CMFD-SIFT

A very common way of image tampering is the copy-move attack. When creating a copy-move forgery, it is often necessary to add or remove important objects from an image. To carry out forensic analysis of such images, various copy-move forgery detection (CMFD) methods have been developed in the literatures. In recent years, many feature-based CMFD approaches have emerged due to its excellent robustness to various transformations. However there is still place to improve performance further. Many of them would suffer from the problem of insufficient matched key-points while performing on the mirror transformed forgeries. Furthermore, many feature-based methods might hardly expose the tempering when the forged region is of uniform texture. In this paper, a novel feature-based CMFD method is proposed. Key-points are detected by using a modified SIFT-based detector. A novel key-points distribution strategy is developed for interspersing the key-points evenly throughout an image. Finally, key-points are descripted by an improved SIFT descriptor which is enhanced for the CMFD scenario. Extensive experimental results are presented to confirm the efficacy.