Steganalysis versus splicing detection

Aiming at detecting secret information hidden in a given image using steganographic tools, steganalysis has been of interest for years. In particular, universal steganalysis, not limited to attacking a specific steganographic tool, is of extensive interests due to its practicality. Recently, splicing detection, another important area in digital forensics has attracted increasing attention. Is there any relationship between steganalysis and splicing detection? Is it possible to apply universal steganalysis methodologies to splicing detection? In this paper, we address these intact and yet interesting questions. Our analysis and experiments have demonstrated that, on the one hand, steganography and splicing have different goals and strategies, hence, generally causing different statistical artifacts on images. However, on the other hand, both of them make the touched (stego or spliced) image different from the corresponding original (natural) image. Therefore, natural image model based on a set of carefully selected statistical features under the machine learning framework can be used for steganalysis and splicing detection. It is shown in this paper that some successful universal steganalytic schemes can make promising progress in splicing detection if applied properly. A more advanced natural image model developed from these state-of-the-art steganalysis methods is thereafter presented. Furthermore, a concrete implementation of the proposed model is applied to the Columbia Image Splicing Detection Evaluation Dataset, which has achieved an accuracy of 92%, indicating a significant advancement in splicing detection.