Image scaling factor estimation based on normalized energy density and learning to rank

Abstract

Over the past years, research on digital image forensics has become a hot topic in multimedia security. Among various forensics technologies, image resampling detection has become a standard detection tool in image forensics. Furthermore, examining parameters of geometric transformations such as scaling factors or rotation angles is very useful for exploring an image's overall processing history. In this paper, we propose a novel image scaling factor estimation method based on normalized energy density and learning to rank, which can not only effectively eliminate the long-known ambiguity between upscaling and downscaling in the analysis of resampling but also accurately estimate the factors of weak scaling, i.e., the scaling factors near 1. Empirical experiments on extensive images with different scaling factors demonstrate the effectiveness of our proposed method.