# DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery
## 4 Experiments and Results
### DRRU-Net Using Leaky ReLU  
The performance of DRRU-Net LR to which the enhanced learning method of the proposed JPEG learning module was applied was evaluated, and the performance was compared by learning the same dataset as CAT-Net with randomly initialized weights. CAT-Net took 6590 s for 1 epoch, which was shorter than RRU-Net. The verification score did not increase during 6 epochs after 13 epochs, the final verification score was 0.927, and the final training loss was 0.010. CAT-Net had the highest verification score, and training was terminated when its training loss was about 9 times lower than that of DRRU-Net. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**DEFACTO**} & \multicolumn{2}{c}{**DEFACTO**} & \multirow{2}{*}{**CASIAv2**} \\  & & **Splicing** & **Copy-Move** & \\ \hline \multirow{2}{*}{RRU-Net} & _Dice_ & 0.306 & 0.062 & 0.081 \\  & Acc & 0.988 & 0.963 & 0.914 \\ \multirow{2}{*}{DRRU-Net} & _Dice_ & 0.830 & 0.652 & 0.547 \\  & Acc & 0.997 & 0.984 & 0.864 \\ \multirow{2}{*}{DRRU-Net TL} & _Dice_ & 0.830 & 0.716 & 0.830 \\  & Acc & 0.997 & 0.992 & 0.967 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of splicing and copy-move forgery detection performance in pixel units. CAT-Net learns both high and low resolution. The benefit of CAT-Net is that it is not only advantageous for pixel-by-pixel detection, but also predicts to make the boundary of the detection areas smoother. However, it sometimes identifies normal regions as forgery regions and the generalization performance of predicting forgery regions was lower than DRRU-Net LR. In Copy-Move, which is not a trained forgery domain, other forgery techniques including splicing and copy-move also exist, and the _Dice_ coefficient gradually decreased in CASIAv2, which has a different forgery processing process. On the other hand, DRRU-Net LR had a higher Dice coefficient in CAISAv2 than in DEFACTO Copy-Move. DRRU-Net LR using leaky ReLU can find valid clues for the detecting a forgery commonly used in the DCT domain, resulting in excellent generalization performance in other datasets even though it has learned fewer datasets. When training from the beginning based on Experiment 4.2, the JPEG learning module is configured as a ringed residual block connected to the learning module by using leaky ReLU. Therefore, the method of strengthening learning in the DCT domain is confirmed to be effective in improving performance in other datasets. Table 4 is a picture comparing the images of CASIAv2 of DRRU-Net LR with those of other networks based on normal prediction. These three images are splicing forgeries. CAT-Net sometimes displays the possibility of forgery even in the normal regions and does not properly display probability of forgery in the forgery regions. DRRU-Net recorded a value similar to the average of _Dice_ coefficient as with CAT-Net, but the false detection rate \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**DEFACTO**} & \multicolumn{2}{c}{**DEFACTO**} & \multirow{2}{*}{**CASIAv2**} \\  & & **Splicing** & & **Copy-Move** \\ \hline \multirow{2}{*}{CAT-Net} & _Dice_ & 0.802 & 0.657 & 0.586 \\  & Acc & 0.997 & 0.991 & 0.832 \\ \multirow{2}{*}{DRRU-Net} & _Dice_ & 0.830 & 0.652 & 0.547 \\  & Acc & 0.997 & 0.984 & 0.864 \\ \multirow{2}{*}{DRRU-Net LR} & _Dice_ & 0.840 & 0.666 & 0.693 \\  & Acc & 0.997 & 0.993 & 0.937 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison among CAT-Net, DRRU-Net, and DRRU-Net LR without transfer learning.