Discriminating natural images and computer generated graphics based on the impact of CFA interpolation on the correlation of PRNU

To discriminate natural images from computer generated graphics, a novel identification method based on the features of the impact of color filter array (CFA) interpolation on the local correlation of photo response non-uniformity noise (PRNU) is proposed. As CFA interpolation generally exists in the generation of natural images and it imposes influence on the local correlation of PRNU, the differences between the PRNU correlations of natural images and those of computer generated graphics are investigated. Nine dimensions of histogram features are extracted from the local variance histograms of PRNU to represent the identification features. The discrimination is accomplished by using a support vector machine (SVM) classifier. Experimental results and analysis show that it can achieve an average identification accuracy of 99.43%, and it is robust against scaling, JPEG compression, rotation and additive noise. Thus, it has great potential to be used in image source pipelines forensics.