# DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery
## 4 Experiments and Results
### RRU-Net-Transfer-Learning DRRU-Net  
The pre-training method of DRRU-Net, which is different from the JPEG learning module pre-training method of [14], is described, and the performance difference between RRU-Net and DRRU-Net without pre-training is verified. In [14], the JPEG learning module is pre-trained by using the DCT stream. This is because pre-training of RGB streams in the forgery domain of HRNet is not meaningful for forgery region detection. As in HRNet, the combination of a typical CNN and a JPEG learning module detects forgery depending on the DCT domain. When RRU-Net, which can detect differences in properties of forgery regions in image acquisition artifacts using RGB streams, is combined with JPEG learning module, the possibility that the weights are biased and optimized depending on the DCT domain is a concern. If the weights of DRRU-Net are trained at once, learning is done to rely on the DCT stream, which can relatively easily find clues of the forgery regions. To prevent this, the RRU-Net that learns RGB streams is trained, and training at epochs where the verification scores no longer increase is stopped. The weight of the down-sampling section of this RRU-Net is transfer-learned to the DRRU-Net. The corresponding weights are fixed without being updated through training, and in DRRU-Net, only the layers of the up-sampling section and of the down-sampling path connected to JALM are learned. This operation enables meaningful features to be extracted from RGB streams, and since JALM does not fix the weights of the included layers, it can show higher feature extraction performance than when the weights are fixed. In this experiment, RRU-Net, DRRU-Net, and DRRU-Net TL are compared. The time for 1 epoch of RRU-Net is 7335 s on average, including training and verification tasks. After training progressed up to 19 epochs, the verification scores do not increase during 6 epochs. The final verification score is 0.681, and the final training loss is 0.308. The time for 1 epoch of DRRU-Net is 9780 s, and the verification scores do not increase for 6 epochs since 6 epochs. The final verification score is 0.903, and the final training loss is 0.090. Compared to RRU-Net, the time required per epoch is longer, but training is terminated in fewer epochs with lower training loss and higher verification scores. DRRU-Net TL is a model in which the weights of the down-sampling blocks of the trained RRU-Net above are transfer-learned and fixed. The average time for 1 epoch is 8997 s. The verification scores do not increase further from 0.901 for 6 epochs since 6 epochs, and the final training loss is 0.090. Although the verification scores and losses of DRRU-Net and TL models trained from scratch are similar, in testing, DRRU-Net TL showed the highest detection rate and the generalization performance for different datasets throughout all experiments. The _Dice_ coefficient of RRU-Net is 0.306 in DEFACTO Splicing set, 0.062 in DEFACTO Copy-Move, and 0.081 in CASIAv2. Pixel-by-pixel accuracies are 0.988, 0.963, and 0.914 for each dataset. The reason for the lower score than expected is the difference in training and evaluation methods in [13]. In the experiment of [13], which used CASIA [18] for training and evaluation, the training datasets were increased with the compression and random reversal techniques to better train the RRU-Net. CASIA and COLUMB contain originals of forgery images. Since the images for training were randomly selected, it is highly likely that the originals of forgery images were used for training or evaluation. In addition, it is difficult to consider it as a clear performance indicator for Splicing detection because more JPEG compression and noise-added images randomly generated using that dataset are used than typical Splicing forgery images of the test dataset.