A universal image forensic strategy based on steganalytic model

Image forensics have made great progress during the past decade. However, almost all existing forensic methods can be regarded as the specific way, since they mainly focus on detecting one type of image processing operations. When the type of operations changes, the performances of the forensic methods usually degrade significantly. In this paper, we propose a universal forensics strategy based on steganalytic model. By analyzing the similarity between steganography and image processing operation, we find that almost all image operations have to modify many image pixels without considering some inherent properties within the original image, which is similar to what in steganography. Therefore, it is reasonable to model various image processing operations as steganography and it is promising to detect them with the help of some effective universal steganalytic features. In our experiments, we evaluate several advanced steganalytic features on six kinds of typical image processing operations. The experimental results show that all evaluated steganalyzers perform well while some steganalytic methods such as the spatial rich model (SRM) 4 and LBP 19 based methods even outperform the specific forensic methods significantly. What is more, they can further identify the type of various image processing operations, which is impossible to achieve using the existing forensic methods.