Image Forgery Detection Based on Motion Blur Estimated Using Convolutional Neural Network

Currently images are key evidences in many judicial or other identification occasions, and image forgery detection has become a research hotspot. This paper proposes a novel motion blur based image forgery detection method, which includes three steps. First, a convolutional neural network (CNN)-based motion blur kernel reliability estimation method is proposed, which is used to determine whether an image patch should be involved in the image forgery detection process. Second, a shared motion blur kernels-based image tamper detection method is proposed to detect whether a group of motion blur kernels are projected from the same 3D camera trajectory effectively. Third, a consistency propagation method is proposed to localize tampered regions efficiently. Experiments on synthetic images and natural images show the availability of the proposed method.