# Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
## 3 Results
Table 7 shows the validation and test accuracy for the training/validation ratios 7:3, 8:2, and 9:1. Although the test accuracy shows an improvement over validation accuracy, there are some inconsistencies. For example, in D-Net, the validation accuracy for the training/validation ratio of 7:3 is 83.8%; it reduces to 76.3% for test accuracy. However, it can be observed that the highest accuracy of 95.4% is achieved by a CNN pre-trained on the AlexNet model with a training/validation ratio of 9:1, whereas, for the modified version, the highest accuracy of 95.7% is achieved with an 8:2 training/validation ratio. U-Net with GOA outperforms all models with 100% test accuracy for a training/validation ratio of 8:2. Figures 11-13 show the bar graphs to compare the accuracy for the validation and test ratios of 7:3, 8:2, and 9:1, respectively. Table 8 shows the specificity, recall, precision, F1 score, and AUC for all the models and the modified models for a training/validation ratio of 7:3. The CNN pre-trained with AlexNet provides the best F1-score and AUC values. However, in the modified versions, the Mask R-CNN with Sobel filter provides the best F1-score and AUC values. U-Net with GOA outperforms all the models. Table 9 shows the specificity, recall, precision, F1 score, and AUC for all the models and the modified models for a training/validation ratio of 8:2. A CNN pre-trained with AlexNet provides the best AUC of 1.0 and CNN-HPF provides the best F1 score of 0.926. However, in the modified versions, the Mask RCNN with Sobel filter provides the best F1-score and AUC values. U-Net with GOA outperforms all the models with an F1 score of 0.991. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Train/Valid Ratio = 7:3**} & \multicolumn{2}{c}{**Train/Valid Ratio = 8:2**} & \multicolumn{2}{c}{**Train/Valid Ratio = 9:1**} \\ \cline{2-7}  & **Valid** & **Test** & **Valid** & **Test** & **Valid** & **Test** \\  & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** \\ \hline D-Net [33] & 83.80\% & 76.30\% & 85.80\% & 87.5\% & 86.81\% & 87.64\% \\ \hline CNN–HPF [43] & 89.22\% & 89.22\% & 92.30\% & 91.80\% & 93.73\% & 75.40\% \\ \hline RRU-Net [45] & 76.13\% & 78.63\% & 76.01\% & 79.51\% & 78.75\% & 79.58\% \\ \hline Mask R-CNN & 80.53\% & 80.53\% & 78.16\% & 64.50\% & 81.28\% & 75.98\% \\ + Sobel filter [43] & 91.63\% & 89.13\% & 93.94\% & 80.94\% & 94.70\% & 95.40\% \\ \hline CNN based on pre-trained & & & & & & \\ Alex Net model [46] & 73.97\% & 61.47\% & 77.49\% & 64.49\% & 84.37\% & 78.54\% \\ \hline Deep Neural & & & & & & \\ Architecture-Buster & 73.97\% & 61.47\% & 77.49\% & 64.49\% & 84.37\% & 78.54\% \\ Net [47] & & & & & & \\ \hline D-Net & 88.91\% & 95.50\% & 89.56\% & 95.06\% & 90.96\% & 93.85\% \\ \hline CNN–HPF & 92.43\% & 96.58\% & 93.14\% & 94.64\% & 95.81\% & 95.95\% \\ \hline RRU-Net & 78.19\% & 88.19\% & 78.95\% & 88.95\% & 81.56\% & 85.73\% \\ \hline Mask R-CNN + Sobel filter & 82.12\% & 85.62\% & 83.50\% & 93.50\% & 85.54\% & 87.21\% \\ \hline CNN based on pre-trained & 92.75\% & 93.25\% & 94.49\% & 95.70\% & 95.38\% & 95.38\% \\ Alex Net model & & & & & & \\ \hline Deep Neural & & & & & & \\ Architecture-Buster Net. & & & & & \\ \hline U-NET network optimized & 94.63\% & 98.13\% & 95.31\% & 100.00\% & 97.98\% & 97.85\% \\ with Grasshopper & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Validation and test accuracy results. Figure 11: Validation and test accuracy results with a 7:3 validation/test ratio. Figure 12: Validation and test accuracy results with an 8:2 validation/test ratio. Table 10 shows the specificity, recall, F1 score, precision, and AUC for all the models and the modified models for a training/validation ratio of 9:1. RRU-Net provides the best AUC of 0.98, and the CNN pre-trained on AlexNet provides the best F1 score of 0.924. However, in the modified versions, the Mask RCNN with Sobel filter provides the best F1-score and AUC values.