# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**Train/Valid = 9:1**} \\  & \multicolumn{8}{c}{**Accuracy of Validation Set for Each Epoch during the Training Process (\%)**} \\ \cline{2-11}  & **E 1** & **E 2** & **E 3** & **E 4** & **E 5** & **E 6** & **E 7** & **E 8** & **E 9** & **E 10** \\ \hline D-Net [33] & 75.73 & 77.36 & 85.8 & 80.54 & 84.04 & 85.8 & 82.23 & 81.96 & 83.14 & 85.01 \\ \hline CNN-HPF [43] & 79.58 & 85.24 & 89.2 & 92.3 & 90.96 & 89.2 & 90.72 & 92.3 & 90.87 & 91.2 \\ \hline RRU-Net [45] & 63.64 & 74.73 & 72.18 & 75.4 & 74.19 & 76.01 & 76.01 & 74.83 & 73.5 & 75.4 \\ \hline Mask R-CNN + Sobel filter [43] & 66.61 & 77.13 & 76.98 & 78.16 & 77.81 & 78.09 & 76.5 & 77.56 & 76.5 & 78.16 \\ \hline CNN based on pre-trained Alex Net model [46] & 77.69 & 86.62 & 89.21 & 92.37 & 91.86 & 89.96 & 93.41 & 93.94 & 93.94 & 92.3 \\ \hline Deep Neural Architecture-Buster Net [47] & 63.86 & 75.12 & 76.29 & 72.04 & 77.49 & 71.69 & 74.03 & 70.97 & 76.68 & 77.49 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Deep Neural Architecture-Buster Net. \\ \end{tabular} } & \multirow{2}{*}{78.03} & \multirow{2}{*}{85.89} & \multirow{2}{*}{89.4} & \multirow{2}{*}{89.65} & \multirow{2}{*}{86.94} & \multirow{2}{*}{90.87} & \multirow{2}{*}{90.96} & \multirow{2}{*}{87.87} & \multirow{2}{*}{85.44} & \multirow{2}{*}{90.89} \\ \cline{2-2} \cline{6-6}  & CNN-HPF & & & & & & & & & \\ \hline CNN-HPF & 82.46 & 90.81 & 91.33 & 92.72 & 92.82 & 95.81 & 93.31 & 93.59 & 95.81 & 92.34 \\ \hline RRU-Net & 67.02 & 78.39 & 80.58 & 80.11 & 80.62 & 81.56 & 78.43 & 80.21 & 81.56 & 81.56 \\ \hline Mask R-CNN + Sobel filter & 74.04 & 83.99 & 81.09 & 85.54 & 80.71 & 83.19 & 83.11 & 85.54 & 85.54 & 83.96 \\ \hline CNN based on pre-trained Alex Net model & 84.96 & 89.35 & 92.13 & 94.27 & 95.38 & 91.99 & 94.73 & 95.38 & 94.97 & 95.38 \\ \hline Deep Neural Architecture-Buster Net. & 71.65 & 81.89 & 81.65 & 86.33 & 82.2 & 83.19 & 79.39 & 83.19 & 82.24 & 86.33 \\ \hline U-NET network optimized with Grasshopper & 85.68 & 89.6 & 94.45 & 92.58 & 92.45 & 97.98 & 94.45 & 97.98 & 95.6 & 97.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Validation accuracy of each epoch during training with train/validation ratio = 8:2. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**Train/Valid = 8:2**} \\  & \multicolumn{8}{c}{**Accuracy of Validation Set for Each Epoch during the Training Process (\%)**} \\ \cline{2-11}  & **E 1** & **E 2** & **E 3** & **E 4** & **E 5** & **E 6** & **E 7** & **E 8** & **E 9** & **E 10** \\ \hline D-Net [33] & 75.73 & 77.36 & 85.8 & 80.54 & 84.04 & 85.8 & 82.23 & 81.96 & 83.14 & 85.01 \\ \hline CNN-HPF [43] & 79.58 & 85.24 & 89.2 & 92.3 & 90.96 & 89.2 & 90.72 & 92.3 & 90.87 & 91.2 \\ \hline RRU-Net [45] & 63.64 & 74.73 & 72.18 & 75.4 & 74.19 & 76.01 & 76.01 & 74.83 & 73.5 & 75.4 \\ \hline Mask R-CNN + Sobel filter [43] & 66.61 & 77.13 & 76.98 & 78.16 & 77.81 & 78.09 & 76.5 & 77.56 & 76.5 & 78.16 \\ \hline CNN based on pre-trained Alex Net model [46] & 77.69 & 86.62 & 89.21 & 92.37 & 91.86 & 89.96 & 93.41 & 93.94 & 93.94 & 92.3 \\ \hline Deep Neural Architecture-Buster Net & 63.86 & 75.12 & 76.29 & 72.04 & 77.49 & 71.69 & 74.03 & 70.97 & 76.68 & 77.49 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Deep Neural Architecture-Buster Net. \\ \end{tabular} } & \multirow{2}{*}{63.81} & \multirow{2}{*}{82.12} & \multirow{2}{*}{80.09} & \multirow{2}{*}{83.01} & \multirow{2}{*}{81.73} & \multirow{2}{*}{80.91} & \multirow{2}{*}{83.01} & \multirow{2}{*}{78.69} & \multirow{2}{*}{78.69} & \multirow{2}{*}{81.46} \\ \cline{2-2} \cline{6-6}  & CNN-HPF & & & & & & & & \\ \hline U-NET network optimized with Grasshopper & 79.55 & 86.38 & 92.50 & 90.65 & 93.96 & 95.31 & 92.45 & 95.14 & 95.31 & 95.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Validation accuracy of each epoch during training with train/validation ratio = 8:2.