Copy move image classification by feature optimization with support vector machine approach
International Journal of Development Research
Copy move image classification by feature optimization with support vector machine approach
Received 14th May, 2017; Received in revised form 25th June, 2017; Accepted 23rd July, 2017; Published online 30th August, 2017
Copyright ©2017, Neha jain and Er. Sushil Bansal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copy-move is a simple and effective operation for creating digital image forgeries, where an area of an image is copied and pasted to a different location in that image. Generally, a forger uses some affine transformations to make the changes visually intact. Most existing copy-move detection methods are not effective when copied regions are under geometrical distortions. In this paper detection and classification by point base and block base features SIFT and SURF Respectively but use ant colony optimization in matching and feature selection phases ,in case of SIFT features and proposed SIFT with ACO features which also use in classification with support vector machine with Gaussian and polynomial kernel.
