# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 3 Results
U-Net with GOA outperforms all the models with an F1 score of 0.98. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Train/Valid Ratio = 7:3**} \\ \cline{2-6}  & **Specificity** & **Recall** & **Precision** & **F1 Score** & **AUC** \\ \hline D-Net & 0.913 & 0.706 & 0.857 & 0.774 & 0.926 \\ \hline CNN–HPF & 0.913 & 0.824 & 0.875 & 0.848 & 0.941 \\ \hline RRU-Net & 0.733 & 1 & 0.714 & 0.833 & 0.81 \\ \hline Mask R-CNN + Sobel filter & 0.933 & 0.8 & 0.889 & 0.842 & 0.927 \\ \hline CNN based on pre-trained & 0.913 & 0.941 & 0.889 & 0.914 & 0.972 \\ Alex Net model & 0.933 & 0.811 & 0.928 & 0.865 & 0.932 \\ \hline Deep Neural Architecture-Buster Net. & 0.939 & 0.789 & 0.893 & 0.968 & 0.968 \\ \hline D-Net & 0.939 & 0.787 & 0.893 & 0.923 & 0.923 \\ \hline CNN-HPF & 0.939 & 0.787 & 0.893 & 0.923 & 0.923 \\ \hline RRU-Net & 0.892 & 0.687 & 0.803 & 0.855 & 0.855 \\ \hline Mask R-CNN + Sobel filter & 0.9 & 0.937 & 0.908 & 0.976 & 0.976 \\ \hline CNN based on pre-trained Alex & 0.956 & 0.766 & 0.918 & 0.923 & 0.923 \\ Net model & & & & & \\ \hline Deep Neural Architecture-Buster Net. & 0.94 & 0.662 & 0.875 & 0.878 & 0.878 \\ \hline U-Net with GOA & 0.956 & 0.937 & 0.957 & 0.986 & 0.986 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical model analyses with train/validation ratio = 7:3. Figure 13: Validation and test accuracy results with a 9:1 validation/test ratio. Table 11 shows the resulting analyses of detecting image forgery on the CASIA dataset using different models. The table includes the train/valid ratio, highest accuracy, and highest AUC for each model. The CNN-HPF model obtained the highest accuracy of 93.73% and the highest AUC of 0.978 for the 9:1 train/valid ratio. The CNN based on the pre-trained Alex Net model achieved the highest accuracy of 95.40% and the highest AUC of 1.00. The U-Net with GOA achieved 100.00% accuracy and an AUC of 0.991. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Train/Valid Ratio = 9:1**} \\ \cline{2-6}  & **Specificity** & **Recall** & **Precision** & **F1 Score** & **AUC** \\ \hline D-Net & 0.9 & 0.8 & 0.8 & 0.8 & 0.928 \\ \hline CNN-HPF & 0.867 & 0.9 & 0.818 & 0.857 & 0.978 \\ \hline RRU-Net & 0.8 & 1 & 0.714 & 0.833 & 0.88 \\ \hline Mask R-CNN + Sobel filter & 0.87 & 0.941 & 0.842 & 0.889 & 0.928 \\ \hline CNN based on pre-trained Alex Net model & 0.961 & 0.911 & 0.937 & 0.924 & 1 \\ \hline Deep Neural Architecture-Buster Net. & 0.967 & 0.857 & 0.968 & 0.909 & 0.938 \\ \hline D-Net & 0.977 & 0.887 & 0.961 & 0.95 & 0.924 \\ \hline CNN-HPF & 0.965 & 0.89 & 0.943 & 0.951 & 0.923 \\ \hline RRU-Net & 0.925 & 0.793 & 0.872 & 0.875 & 0.875 \\ \hline Mask R-CNN & 0.933 & 0.943 & 0.943 & 0.978 & 0.978 \\ \hline CNN based on pre-trained Alex Net model & 0.974 & 0.883 & 0.956 & 0.954 & 0.954 \\ \hline Deep Neural Architecture-Buster Net & 0.962 & 0.818 & 0.933 & 0.905 & 0.905 \\ \hline U-Net with GOA & 0.967 & 0.986 & 0.972 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Statistical model analyses with train/validation ratio = 9:1.