# Deep Learning Methods to Detect Image Falsification
## 4 Experiments and Results
### Model Parameter Experiment
The best results are obtained with a block size of 60, whereas the worst results are observed with a block size of 30. The results for a block size of 120 fall approximately in the middle. For example, a network with a block size of 60 achieves an accuracy of 92.4%, whereas the accuracy is 89.46% with a block size of 30. With a block size of 120, the accuracy is 91.86% (see Table 9). Observations with the Adam optimizer (refer to Figure 6) indicate that the choice of block size and learning rate significantly influences the F1 score. Generally, increasing the block size from 30 to 60 results in an enhancement in the F1 score. However, further increases up to 120 do not consistently yield positive effects on the F1 score. This behavior might be attributed to the larger block size, facilitating the model's understanding of longer contexts, but excessively large block sizes could adversely affect training due to the increased complexity and memory requirements. Regarding the learning rate, the F1 scores' distribution is quite diverse, suggesting that the learning rate does not have a significant influence. In contrast, with RMSprop, we observe that the learning rate has \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Optimizer** & **BS** & **LR** & **g1 Accuracy** & **g2 Accuracy** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline [PERSON] & 30 & 0.01 & 52.8728 & 83.9425 & 89.4667 & 96.8176 & 98.2092 & 87.9325 & 92.7872 \\ [PERSON] & 120 & 0.01 & 53.0571 & 89.9248 & 91.8667 & 92.7979 & 94.6655 & 94.3460 & 94.5055 \\ RMSProp & 30 & 0.001 & 74.3008 & 86.6768 & 92.6000 & 96.6964 & 92.9365 & 98.8186 & 95.7873 \\ RMSProp & 120 & 0.001 & 73.2480 & 84.6638 & 89.7333 & 93.6729 & 97.7473 & 84.2194 & 90.4805 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Key results on learning rate and block size impact on AUC. LR—learning rate, BS—block size. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Optimizer** & **BS** & **LR** & **g1 Accuracy** & **g2 Accuracy** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline Adam & 60 & 0.005 & 51.8731 & 88.6591 & 94.6000 & 97.4530 & 97.6644 & 95.2743 & 96.4545 \\ [PERSON] & 30 & 0.001 & 70.4137 & 82.2759 & 87.7333 & 94.4294 & 96.2072 & 87.7637 & 91.7917 \\ Adam & 30 & 0.01 & 52.8728 & 83.9425 & 89.4667 & 96.8176 & 98.2092 & 87.9325 & 92.7872 \\ Adam & 60 & 0.01 & 53.2641 & 88.5757 & 92.4000 & 96.8222 & 98.0961 & 91.3080 & 94.5804 \\ Adam & 120 & 0.01 & 53.0571 & 89.9248 & 91.8667 & 92.7979 & 94.6655 & 94.3460 & 94.5055 \\ RMSProp & 60 & 0.005 & 54.3027 & 89.4659 & 93.8000 & 96.3990 & 94.8971 & 97.2996 & 96.0833 \\ RMSProp & 120 & 0.01 & 54.3165 & 22.9941 & 21.0000 & 50.0000 & 0.0000 & 0.0000 & 0.0000 \\ RMSProp & 30 & 0.001 & 72.9247 & 85.8949 & 91.0000 & 94.7229 & 92.2705 & 96.7089 & 94.4376 \\ RMSProp & 60 & 0.001 & 75.9366 & 87.5371 & 92.0000 & 94.3806 & 93.9819 & 96.2025 & 95.0792 \\ RMSProp & 120 & 0.001 & 70.9933 & 87.7717 & 90.4000 & 91.7142 & 95.3971 & 89.1983 & 92.1936 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Key results on learning rate and block size impact on accuracy. LR—learning rate, BS—block size. In Figure 7, when examining the data for Adam, it can be noticed that higher dropout (0.6-0.7) with a block size of 60 generally leads to higher AUC values. For example, with a dropout of 0.7 and a block size of 60, the AUC reaches 97.45%, and it is 97.29% with a dropout of 0.6. However, worse AUC values are obtained with longer block sizes. For example, with a dropout of 0.7 and a block size of 120, the AUC is 95.51%, whereas it is 93.91% with a dropout of 0.6. When the dropout is lower (0.3-0.4), worse results are obtained. For example, with a block size of 30 and a dropout of 0.7, the AUC is 97.11%, whereas it is 95.93% with a dropout of 0.3. With RMSprop, lower AUC values are obtained with a block size of 120, whereas it depends on the dropout with other block size combinations.