# DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery
## 2 Related Works  
DEFACTO [26] is a collection of forgery image datasets including the image forgery domains Splicing, Copy-Move, Removal, and Morphing. Some of the splicing datasets and copy-move datasets among DEFCATO are used in the experiment. CASIAv2 [27] is a collection of datasets that include a set of authenticated images before forgery and a set of forgery images in which splicing and copy-move images are mixed. U-Net [24], an underlying model of RRU-Net, outperformed existing methods in the International Symposium on Biomedical Imaging (ISBI) challenge for the segmentation of neural structures in electron microscopy stacks in 2015. U-Net is divided into the contracting path and expanding path, and the two paths are symmetrical. The output of each contracting-path block is followed by the up-sampling result of the previous output of the symmetric expanding-path block, and then connecting to the input. This allows high-resolution capabilities to achieve accurate localization while reducing the loss of detailed information. ResNet [28] is the network which won the ImageNet Classification Challenge in 2015. Residual mapping was first proposed in ResNet and is defined as Equation (1). \[y=F(x)+x \tag{1}\]  
In Equation (1), \(x\) is the input of the layer and \(y\) is the output. \(F(x)+x\) performs addition by connecting the input and its operation result with a shortcut (skip) connection. Residual mapping and shortcut connection have been actively used in CNN research to relieve the gradient descent problem. RRU-Net applies the ringed residual architecture proposed in the paper of the U-Net architecture. The ringed residual architecture utilizes the residual architecture of ResNet. It is a building block architecture in which two residual propagations (forward direction) and one residual feedback propagation (reverse direction) are performed instead of one residual propagation in a block where two convolution layers are connected. This can be expressed as shown in Equations (2) and (3). \[y_{f}=F(x,\ \ W_{i})+W_{\text{s}}\times x \tag{2}\]  
\[y_{b}=(\text{s}(G(y_{f})+1))\times x \tag{3}\]  
\(y_{f}\) is the output of the building block, \(W_{i}\) is the weight of layer \(i\), and \(F(x,W_{i})\) represents the residual mapping to be learned. The function \(F\) is \(W_{2}\sigma(W_{1}\times x)\). \(\sigma\) represents the activation function ReLU. \(W_{\text{s}}\times x\) is a linear projection operation, which is executed in the shortcut connection and element units, and sets the dimension to the next input with kernel size 1. \(y_{b}\) becomes the enhanced input \(\times\) of the next \(y_{f}\) with the residual feedback operation. In the Equation (3), \(y_{b},y_{f}\) is the output of the residual propagation. Function \(s\) is a sigmoid activation function, and function \(G\) sets the dimension of \(y_{f}\) to the following inputs by linear projection. This method enables it to reduce the gradient descent problems and amplify the differences of the image's intrinsic properties between the normal regions and the forgery regions in the forgery image. Although the validity of the ringed residual architecture in RRU-Net was proven, the detection accuracy was limited to about 80% when only RGB streams were used to detect forgery regions. Therefore, in DRRU-Net, in this paper, the weights of the generation are saved before the verification scores increase and then decrease in the training process of RRU-Net, and a transfer learning model that trains only the blocks of the down-sampling path and up-sampling path, which is connected to JALM, is designed. By doing so, the performance could be improved. CAT-Net learns without losing the distribution and spatial characteristics of DCT coefficients for the first time. The RGB stream passes through HRNet and learns image acquisition artifacts from the images such as sensor pattern noise, EXIF metadata, and visual content.