Detectability of the Image Operation Order: Upsampling and Mean Filtering

As image modification and tampering, especially multiple editing operations, prevail in today's world, identifying authenticity and credibility of digital images becomes increasingly important. Recently, two editing operations, upsampling and mean filtering, have attracted increasing attention. While there are many existing image forensics techniques to identify the existence and order of specific operations in a certain processing chain, few detecting methods are concerned about the order of upsampling and mean filtering operations. Following some strongly indicative analysis in different domains of DFTs of images' p-maps, this paper discusses a newly designed method which utilizes features to determine the order of upsampling and mean filtering operations. Specifically, our goal is to use two features, the symmetry-based PSNR and the fourth order energy fitting curve, to characterize the features of operation chains in the DFTs of images' p-maps. We calculate the variance of the fitting curve and examine the change of fingerprints under different operating intensities to ensure these two features can be broadly applied to operation detection. These features are fed to SVM, effectively discriminating among five combinations of upsampling and mean filtering. The representative experiments can verify the effectiveness of the proposed method.