DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery

Abstract

In this paper, we propose a lightweight deep learning network (DRRU-Net) for image-splicing forgery detection. DRRU-Net is an architecture that combines RRU-Net for learning the visual content of images and image acquisition artifacts, and a JPEG artifact learning module for learning compression artifacts in the discrete cosine transform (DCT) domain. The backbone model of a network based on pre-training, such as CAT-Net, a representative network for image forgery detection, has a relatively large number of parameters, resulting in overfitting in a small dataset, which hinders generalization performance. Therefore, in this paper, the learning module is designed to learn the characteristics according to the DCT domain in real time without pre-training. In the experiments, the proposed network architecture and training method of DRRU-Net show that the network parameters are smaller than CAT-Net, the forgery detection performance is better than that of RRU-Net, and the generalization performance for various datasets can be improved.