Image Splicing Forgery Detection Combining Coarse to Refined Convolutional Neural Network and Adaptive Clustering

Abstract This paper proposes a splicing forgery detection method with two parts: a coarse-to-refined convolutional neural network (C2RNet) and diluted adaptive clustering. The proposed C2RNet cascades a coarse convolutional neural network (C-CNN) and a refined CNN (R-CNN) and extracts the differences in the image properties between un-tampered and tampered regions from image patches with different scales. Further, to decrease the computational complexity, an image-level CNN is introduced to replace patch-level CNN in C2RNet. The proposed detection method learns the differences of various image properties to guarantee a stable detection performance, and the image-level CNN tremendously decreases its computational time. After the suspicious forgery regions are located by the proposed C2RNet, the final detected forgery regions are generated by applying the proposed adaptive clustering approach. The experiment results demonstrate that the proposed detection method achieves relatively promising results compared with state-of-the-art splicing forgery detection methods, even under various attack conditions.