Copy-move forgery detection using hybrid transform and K-means clustering technique

Currently, techniques such as copy-move forgery or image cloning and image splicing are used for digital image forgery. Out of these forgeries, copy-move forgery is the most common and popular approach because it involves only one image. Copy-move forgery is easy to create and very hard to detect. Thus, there is need of an efficient copy-move forgery detection technique to verify the authenticity and integrity of images. Copy-move forgery detection techniques are broadly of two types namely Block-based methods and Key-point based methods. In this paper, hybrid transform and K-means clustering technique based algorithm has been proposed to detect copy-move forgery in digital images. The application of hybrid transform allows the reduction of image features. Experimental results showed that the presented copy-move forgery detection algorithm is effective both in time and accuracy as compared to the existing algorithms.