# Deep Learning Methods to Detect Image Falsification
## 4 Experiments and Results  
### Model Parameter Experiment  
The obtained results were grouped according to the optimizer used, and the average, standard deviation, and coefficients of variation in the results were calculated (see Table 4). The SGD optimizer shows the lowest accuracy, AUC, precision, recall, and F1 values compared to the other two optimizers. Additionally, the variation coefficients are quite large, indicating a greater distribution of results. The Adam optimizer shows the best results in all measurement indicators: highest accuracy, AUC, precision, recall, and F1. Additionally, the variation coefficients for this optimizer are smaller, indicating a smaller distribution of results, which is desirable. The RMSprop optimizer shows better results than SGD but worse than Adam. Its accuracy, AUC, precision, recall, and F1 indicators are good, but the variation coefficients are higher than those for the Adam optimizer but lower than those for SGD. Based on this analysis, the Adam optimizer is the best choice for further analysis because it has the highest accuracy, AUC, precision, recall, and F1, and its variation coefficients are smaller, indicating a smaller distribution of results. RMSprop is in second place, and the SGD optimizer is unsuitable in this situation due to its lower performance and \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Optimizer—Parameter** & **g1 Accuracy** & **g2 Accuracy** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline “SGD”—average & 0.3358 & 0.3849 & 0.2696 & 0.5009 & 0.1629 & 0.1003 & 0.1051 \\ “SGD”—standard deviation & 0.1799 & 0.2487 & 0.1531 & 0.0054 & 0.3310 & 0.2615 & 0.2580 \\ “SGD”—variation coefficient & 53.5699 & 64.6357 & 56.7945 & 1.0917 & 203.0915 & 260.5321 & 245.4264 \\ “Adam”—average & 0.5778 & 0.8688 & 0.9143 & 0.9572 & 0.9698 & 0.9065 & 0.9368 \\ “Adam”—standard deviation & 0.1088 & 0.0214 & 0.0172 & 0.0137 & 0.0084 & 0.0269 & 0.0140 \\ “Adam”—variation coefficient & 18.8349 & 2.4706 & 1.8836 & 1.4382 & 0.8703 & 2.9699 & 1.5017 \\ “RMSprop”—average & 0.6084 & 0.7499 & 0.7972 & 0.8427 & 0.8358 & 0.8025 & 0.8069 \\ “RMSprop”—standard deviation & 0.0898 & 0.2504 & 0.2328 & 0.1734 & 0.2684 & 0.3124 & 0.3010 \\ “RMSprop”—variation coefficient & 14.7599 & 33.3940 & 29.2101 & 20.5766 & 32.1170 & 38.9303 & 37.3123 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experiment result comparison by optimizers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compression Level** & **g1 Accuracy** & **g2 Accuracy** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** & **Time in Seconds** \\ \hline “raw” & 86.3806 & 91.7724 & 95.7143 & 97.8705 & 96.9965 & 98.0357 & 97.5133 & 898.1953 \\ “c23” & 63.5757 & 87.1847 & 92.9333 & 96.8073 & 96.7629 & 93.3333 & 95.0172 & 887.5831 \\ “c40” & 81.5299 & 82.444 & 91.28  
In Figure 1, we can see that the AUC values are relatively high and range between 92.79% and 97.45% with the Adam optimizer. The AUC values slightly vary depending on the learning rate and dropout. Furthermore, it can be concluded that a model that uses a higher learning rate and dropout (for example, a learning rate of 0.005 and dropout of 0.7) can achieve a better AUC value. With the RMSprop optimizer, lower dropout percentages (0.3-0.4) and lower learning rates (0.001) tend to result in higher AUC values. On the other hand, combinations of higher dropout percentages (0.6-0.7) and higher learning rates (0.01) tend to result in lower AUC values, which may indicate that these combinations can cause too much fluctuation during training and hinder the model's learning ability. Based on these findings, it can be concluded that selecting the appropriate learning rate and dropout percentage combination has a significant impact on the AUC variations. Properly selecting these parameters can improve the model's performance and increase AUC values (see Table 5).