# DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery
## 1 Introduction  
As the development of digital image-editing technology and image-editing software make them accessible to the public, the detection of the increasing number of forgery images has emerged as an important issue, and research in the field of digital forensics for detecting a digital image forgery has been actively conducted as well [1; 2; 3; 4]. Image forgery methods include splicing, copy-move, object removal, and morphing [5; 6; 7]. Typical image forgery methods and characteristics are as follows:  
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* Splicing: copying parts of one image and pasting them into another image;
* Copy-Move: copying parts of one image and pasting them into the same image;
* Object Removal: removing objects within an image and filling the space to fit the surrounding background;
* Morphing: combining the features of two images and creating a distorted and mixed image. Image splicing pastes parts of a picture using image-editing software under the condition of two or more images prepared. At this time, different images contain information about the environment in which the picture was taken (illuminance, lens, noise, etc. ), and that information may be impossible to visually recognize. The development of image-editing software makes more sophisticated splicing forgery possible by adjusting the illuminance of the background image, adjusting the direction of shadows, and retouching boundaries, beyond simply pasting images. Figure 1 shows an example of splicing forgery. The detection method of the image-splicing forgery regions has evolved from the traditional method of detecting with a mathematical algorithm based on discrete cosine transform (DCT) to a method of detecting by using a deep learning network, Convolutional Neural Network (CNN) [8; 9; 10]. DCT is one of the digital orthogonal transform coding methods for image coding and is also used for JPEG compression. DCT decomposes a block of pixels into high-frequency and low-frequency components. Human vision is insensitiveto the high-frequency information of an image, resulting in compressing the image by removing high-frequency components. When an edited image is compressed and saved again, a compression trace remains on the image. This enables single and double JPEG compression to be detected. It has already been proven that DCT can be used to find forensic clues in image forensics through the traditional splicing forgery detection method [11, 12]. However, it has not achieved notable results on the localization of the forgery region. In recent years, CNN is confirmed to have excellent performance for the image feature extraction [13, 14, 15], and research is being conducted to classify forgery images, to detect forgery regions in pixel units, and to localize them by using CNN [16, 17]. The segmentation method for forgery region localization using CNN has been studied by learning image acquisition artifacts that can be obtained from images, by learning compression artifacts from DCT domains [18, 19, 20], or by learning domains that can be collected from images together. Mantranet [21] and RRU-Net [22], released in 2019, perform forgery detection and semantic segmentation by inputting only the RGB streams of images. The accuracy of the detection of the public dataset used in that study was 80%, which is not considered high performance as of today, but it was the performance accuracy when using only the RGB stream. Therefore, it is expected to be applied in various ways for further research because it is excellent in feature extraction for the forgery regions. CAT-Net [23], released in 2022, further searches the compression artifacts. It is a network that uses HRNet [24], which has proven its excellent performance in computer vision tasks, such as object detection and human pose estimation, as a backbone and learns by inputting RGB and DCT streams. It showed significantly higher performance than other networks for eight out of the nine public datasets used in that study. However, this comparison is not considered appropriate.