# DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery
## 1 Introduction
While the HRNet of CAT-Net uses weights pre-trained with the ImageNet [25] dataset, the JPEG artifact learning module (JALM) is pre-trained with a customized dataset. In the paper, the CAT-Net w/o D.P learned with the weights of CAT-Net, initialized randomly, showed significantly lower localization performance than the pre-trained CAT-Net. Pre-training on the double JPEG compression of JALM is recommended to improve localization performance in further studies. Since the HRNet of CAT-Net is a method of learning RGB streams, it does not learn meaningful features of the forgery regions. Considering the results of the performance experiment on whether or not JALM learning in the DCT domain is pre-training, it can be seen that CAT-Net is highly dependent on the DCT domain. In the study of [22], the effectiveness of the ringed residual architecture has been demonstrated, but there are limitations in using only RGB streams to detect forgery regions. To overcome this, the best network for image forgery detection was created in the study of [23], but there is no public dataset for the pre-training of JALM, making it difficult to compare between studies. A dataset needs to be prepared for pre-training and a network separately built for pre-training JALM. In addition, since the performance is dependent on the pre-training, the pre-training is compulsory. In this paper, we propose DRRU-Net, which combines the JALM of CAT-Net with RRU-Net as the backbone. DRRU-Net is an architecture that combines RRU-Net for learning  
Figure 1: Example of an image-splicing forgery: (**a**) object image; (**b**) target background; (**c**) splicing forgery; (**d**) ground truth for forgery region. the visual contents of images and image acquisition artifacts, and JALM for learning compression artifacts in the DCT domain. Since the down-sampling path is the same as RRU-Net, the weights of the down-sampling path of DRRU-Net can be initialized after pre-training with RRU-Net. An easy pre-training method different from RRU-Net for improved results was described. Pre-training also works even when using the same dataset as the proposed network. Experiments verified high generalization performance for other datasets even with learning a small dataset. The main contributions of this paper are the following:  
* A network of a universally available simple architecture was designed to compensate for the above disadvantages of RRU-Net and CAT-Net. * A new network, DRRU-Net, which combines the JALM of CAT-Net with RRU-Net as the backbone was proposed and the architecture of DRRU-Net and an easy pre-training method different from CAT-Net for improved results was described. * Pre-training also works even when using the same dataset as the proposed network. * Experiments verified high generalization performance for other datasets even with learning a small dataset. The remainder of this paper is structured as follows: Section 2 describes the dataset used in DRRU-Net, and the underlying networks. In Section 3, the architecture and algorithm of DRRU-Net are explained, and in Section 4, the performance evaluation experiments of DRRU-Net and their results are explained. In the first experiment in Section 4, a pre-training method to enhance the search for visual cues and image acquisition artifacts in RGB streams is proposed. In the second experiment, a method to enhance the search algorithm for the intrinsic property difference of DCT streams for the training of DRRU-Net with randomly initialized weights without pre-training is proposed. The domain of the training dataset used in the experiment is Splicing, and the performance evaluation is performed using Splicing datasets, Copy-Move datasets, and datasets that include both of them. In the experiments, the generalization performance differentiated from the previously released network is verified by comparing the copy-move forgery detection performance, not the training domain.