Time-efficient spliced image analysis using higher-order statistics

Abstract

Image forgery is gaining huge momentum as changing the content is no longer arduous. One of the leading techniques of this category is image splicing. This technique generates a composite image formed by combining regions of images. Once the image is forged, it becomes nearly impossible for the human expert to substantiate. Hence, for detecting and localizing the spliced region in the forged image, a tool is to be developed which has become the need of the hour. Articles have been reported that one of the key ingredients for such a tool is noise inconsistency, among others. The spliced region contains the non-homogeneous distribution of noise which acts as a feature to localize it. State-of-the-art techniques based on inconsistent noise are suffering from challenges like the requirement of prior knowledge about the image, localization of spliced region and estimation of inconsistent non-gaussian noise. In this paper, a blind local noise estimation technique has been introduced using a fourth-order central moment to localize the spliced region. This paper tries to overcome the challenges of state-of-the-art techniques. Experimental analysis has been done on images of three publicly available datasets. The results are evaluated on pixel level using confusion matrix and some other performance measures. The result of the given approach is compared with previously reported techniques and found better than them. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.