A Generic Approach Towards Image Manipulation Parameter Estimation Using Convolutional Neural Networks

Belhassen Bayar, Matthew C. Stamm

Abstract: Estimating manipulation parameter values is an important problem in image forensics. While several algorithms have been proposed to accomplish this, their application is exclusively limited to one type of image manipulation. These existing techniques are often designed using classical approaches from estimation theory by constructing parametric models of image data. This is problematic since this process of developing a theoretical model then deriving a parameter estimator must be repeated each time a new image manipulation is derived. In this paper, we propose a new data-driven generic approach to performing manipulation parameter estimation. Our proposed approach can be adapted to operate on several different manipulations without requiring a forensic investigator to make substantial changes to the proposed method. To accomplish this, we reformulate estimation as a classification problem by partitioning the parameter space into disjoint subsets such that each parameter subset is assigned a distinct class. Subsequently, we design a constrained CNN-based classifier that is able to extract classification features directly from data as well as estimating the manipulation parameter value in a subject image. Through a set of experiments, we demonstrated the effectiveness of our approach using four different types of manipulations.