# Deep Learning Methods to Detect Image Falsification
## 4 Experiments and Results
### Model Parameter Experiment
It can also be concluded that a model that uses a smaller block size and a higher learning rate (for example, input length of 30-60 and learning rate of 0.005-0.01) can achieve a better AUC value. For instance, when the block size is 30 and the learning rate is 0.01, the AUC is 96.81%, whereas it decreases to 92.79% when the block size is 120, which is almost 4% lower. With RMSprop, it can be observed that combinations of shorter block sizes (30) and lower learning rates (0.001) tend to result in higher AUC values. Conversely, combinations of longer input lengths (120) and higher learning rates (0.01) result in lower AUC values (see Table 8). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Optimizer** & **d** & **LR** & **g1 Accuracy** & **g2 Accuracy** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline Adam & 0.7 & 0.005 & 51.8731 & 88.6591 & 94.6000 & 97.4530 & 97.6644 & 95.2743 & 96.4545 \\ Adam & 0.3 & 0.001 & 70.4137 & 82.2759 & 87.7333 & 94.4294 & 96.2072 & 87.7637 & 91.7917 \\ RMSProp & 0.6 & 0.005 & 54.3027 & 89.4659 & 93.8000 & 96.3909 & 94.8971 & 97.2996 & 96.0833 \\ RMSProp & 0.7 & 0.01 & 54.3165 & 22.9941 & 21.0000 & 50.0000 & 0.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Key results on learning rate and dropout impact on Accuracy. LR—learning rate, d—dropout. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Optimizer** & **d** & **LR** & **g1 Accuracy** & **g2 Accuracy** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline Adam & 0.7 & 0.005 & 51.8731 & 88.6591 & 94.6000 & 97.4530 & 97.6644 & 95.2743 & 96.4545 \\ Adam & 0.6 & 0.001 & 68.1292 & 87.2029 & 91.4000 & 94.2254 & 97.5728 & 84.8101 & 90.7449 \\ RMSProp & 0.3 & 0.005 & 62.9173 & 88.4458 & 93.7333 & 96.3850 & 95.4281 & 96.8776 & 96.1474 \\ RMSProp & 0.7 & 0.01 & 54.3165 & 22.9941 & 21.0000 & 50.0000 & 0.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Key results on learning rate and dropout impact on F1. LR—learning rate, d—dropout. Figure 4: Learning rate and block size impact on AUC. (**a**)—Adam optimizer; (**b**)—RMSprop—optimizer. Figure 5: Learning rate and block size impact on accuracy. (**a**)—Adam optimizer; (**b**)—RMSprop—optimizer. Figure 6: Learning rate and block size impact on F1. (**a**)—Adam optimizer; (**b**)—RMSprop—optimizer. For instance, with a block size of 30 and a learning rate of 0.001, the AUC is 96.69%. However, it decreases to 93.67% when the block size increases to 120. From these observations, we can conclude that an appropriate combination of learning rate and block size significantly influences the variation in the AUC. From Figure 5, it is evident that the highest accuracy (93.8%) was achieved with a learning rate of 0.005 and a block size of 60 when using RMSprop. Conversely, the lowest accuracy (21%) was observed when using a learning rate of 0.01 across all block sizes (30, 60, and 120). When employing Adam, the highest accuracy result (94.6%) was obtained with a learning rate of 0.005 and a block size of 60. On the other hand, the lowest accuracy (87.33%) was achieved with a learning rate of 0.001 and a block size of 30. The block size does not have a significant impact on the results when using the RMSprop optimizer. Generally, all block size options yield similar performance, but slightly worse results are observed with a block size of 120. For instance, a network with a learning rate of 0.001 and a block size of 120 achieves an accuracy of 90.4%. With a block size of 30, the accuracy is 91%, and the accuracy is 92% with a block size of 60. On the other hand, the learning rate does have an impact, with poorer results observed when using a learning rate of 0.01. With the Adam optimizer, it can be observed that the learning rate has a lesser impact on the results. The results across different learning rates are relatively similar. However, the block size has a more pronounced effect.