DIGITAL SPLICING DETECTION USING LOCAL INVARIANT FEATURES

Abstract

Digital images have become the most important source of information. Due to presence of various image editing tools, images can be easily changed and altered. Therefore, the authentication of digital images has become an important issue. Forgery is performed by copying one part of an image somewhere else in same image. Copied part can be rotated, scaled or cropped while duplicating. So it is necessary to distinguish between authentic and forged images. These techniques are divided into two varieties- one being active i.e. intrusive. It means one needs to embed something in image example watermark, if the image is modified then the embedded data is also modified. Another one is passive i.e non-intrusive. It is a signature based technique. The work presents and compares feature selection algorithms for the detection of image forgery in tampered images. Various features are extracted from normal and spliced using spatial gray level dependence method and many more. Support vector machine has been used for classification. A very difficult problem in classification techniques is the choice of features to distinguish between classes. The feature optimization problem is addressed using a genetic algorithm (GA) as a search method. Classical sequential methods and floating search algorithm are compared against the genetic approach in terms of the best recognition rate achieved and the optimal number of features.