A learning-to-rank approach for image scaling factor estimation

• This paper proposes a learning-to-rank framework of image scaling factor estimation.
• Difference of two features of the ordered image pairs are used to train rank values.
• Scaling factor of a revised image can be evaluated according to the learned rank value.

Over the past years, digital image forensics has become a hot topic in multimedia security field. Among various image forensics technologies, image resampling detection is a standard detection tool. Furthermore, examining parameters of geometric transformations such as scaling factors or rotation angles is very useful for exploring the overall manipulation history of an image. In this paper, we propose a learning-to-rank approach for automatically estimating the scaling factor based on the normalized energy density features and moment features. Specifically, the difference of these features of the ordered image pairs are used for training, then the scaling factor of a new input image can be evaluated from its corresponding rank values. Our proposed method 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 downscaling and weak scaling, i.e., the scaling factors near 1. Empirical experiments on extensive images with different scaling factors demonstrate the superiority of our proposed method when compared with the state-of-the-art methods.