# Deep Learning Methods to Detect Image Falsification
## 4 Experiments and Results
### Model Parameter Experiment
Using the Adam optimizer, the highest AUC is achieved with a learning rate of 0.005 and dropout of 0.7, resulting in an AUC of 97.45%, whereas the lowest AUC is achieved with a learning rate of 0.01 and dropout of 0.6, resulting in an AUC of 92.79%. With the RMSprop optimizer, the highest AUC is achieved with a learning rate of 0.005 and dropout of 0.4, resulting in an AUC of 96.82%, whereas the lowest AUC is achieved with a learning rate of 0.01 and dropout of 0.4, resulting in an AUC of 50%. Overall, the Adam optimizer shows better results on average. In Figure 2, using the Adam optimizer, when the learning rate is 0.005 and dropout rate is 0.7, the model achieves the highest accuracy result (94.6%), whereas the lowest accuracy (87.73%) is obtained with a learning rate of 0.001 and dropout rate of 0.3. With RMSprop, the highest accuracy (93.8%) was achieved with a learning rate of 0.005 and dropout rate of 0.6, whereas the lowest accuracy (21%) was obtained with a learning rate of 0.01 and dropout rate of 0.7. Overall, all Adam optimizer results provide good results, whereas the RMSprop results are highly variable. \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 & 0.01 & 53.0571 & 89.9248 & 91.8667 & 92.7979 & 94.6655 & 94.3460 & 94.5055 \\ RMSProp & 0.4 & 0.005 & 57.3184 & 86.6276 & 92.8667 & 96.8271 & 94.7631 & 96.2025 & 95.4774 \\ RMSProp & 0.4 & 0.01 & 54.4411 & 22.7191 & 21.0000 & 50.0000 & 0.0000 & 0.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Key results on learning rate and dropout impact on AUC. LR—learning rate, d—dropout. Figure 3: Learning rate and dropout impact on F1. (**a**)—Adam optimizer; (**b**)—RMSprop—optimizer. With the Adam optimizer, lower dropout rates generally result in better accuracy, whereas higher dropout rates (0.7) and lower learning rates (0.001) can decrease accuracy. Both learning rate and dropout rate have an impact on the model's accuracy indicators. When analyzing the presented data with RMSprop, we can also see that different learning rates and dropout rates have varying effects on the model's accuracy. For example, a network with a learning rate of 0.01 generally has poorer accuracy results (even up to 21%), whereas lower dropout rates tend to produce better accuracy (see Table 6). In Figure 3, the data show that different values of learning rate and dropout have a small effect on the F1 score. For example, with the Adam optimizer, using a dropout of 0.7 and a learning rate of 0.005 gives the highest F1 score of 96.45%. Meanwhile, with a dropout of 0.6 and a learning rate of 0.001, the F1 score decreases to 90.74%. Overall, the F1 scores for the Adam optimizer are sufficiently high, but the data show that it is difficult to say whether the learning rate and dropout have a certain influence on the F1 score. However, it can be noted that the F1 score is lower with lower learning rate values (0.001). The results with the RMSprop optimizer are highly variable. Using a dropout of 0.3 and a learning rate of 0.005 gives the highest F1 score of 96.14%. Meanwhile, with a dropout of 0.7 and a learning rate of 0.01, the F1 score drops to 0%. Overall, the F1 scores for the RMSprop optimizer are widely dispersed, but the data show that the learning rate and dropout have a certain influence on the F1 score. Lower dropout rates and lower learning rates lead to better results. Comparing Adam and RMSprop, higher learning rate values in both optimizers resulted in worse F1 scores (see Table 7). Figures 4-6 compare the impact of block size and learning rate. In Figure 4, we can see from the Adam data that it can be observed that AUC values vary depending on the learning rate and the block size.