Image splicing detection using singular value decomposition

Abstract

The use of digital images in criminal activities is common because they can be easily manipulated with the application of various available software tools. Image splicing is a common operation for image forgery. In order to detect the spliced images, several methods utilizing the statistical features of the digital images were proposed. In this study, an efficient, singular value, decomposition-based feature extraction method for image splicing detection is presented. Kernel Principal Component Analysis is also applied as classifier feature preprocessor to improve the classification process; and finally, support vector machine is used to distinguish the authenticated and spliced images. The results show a detection accuracy of 98.78% for the proposed method with only 50-dimensional feature vector.