# Deep Learning Methods to Detect Image Falsification
## 4 Experiments and Results
### Model Parameter Experiment
For example, with a dropout of 0.3 and a block size of 60, the AUC reaches 96.38%, whereas it is 95.39% with a dropout of 0.7 and a block size of 30. The highest values are obtained with a block size of 60 and a low dropout or with a smaller block size of 30 and a higher dropout (0.6-0.7). The results on dropout and block size impact on AUC are presented in Table 11.  
In Figure 8, the data for the Adam optimizer reveals that the dropout rate has a lesser impact compared to the block size. Generally, higher accuracy values are achieved with a block size of 60. For instance, with a dropout rate of 0.7 and a block size of 60, the accuracy reaches 94.6%. Conversely, with different block sizes, lower accuracy values are observed. For example, with a dropout rate of 0.7 and a block size of 120, the accuracy reaches 92.2%, whereas it is 89.13% with a dropout rate of 0.3 and a block size of 30. With RMSprop, the dropout rate also has a lesser impact compared to the block size. However, better accuracy is obtained with smaller block sizes, specifically in the range of 30-60. For instance, with a dropout rate of 0.3 and a block size of 60, the accuracy reaches 93.73%, whereas it is 86.46% with a block size of 120. However, with a dropout rate of 0.7 and a block size of 120, the accuracy improves to 90.6%. Results are presented in Table 12. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Optimizer** & **BS** & **d** & **g1 Accuracy** & **g2 Accuracy** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline Adam & 60 & 0.7 & 51.8731 & 88.6591 & 94.6000 & 97.4530 & 97.6644 & 95.2743 & 96.4545 \\ [PERSON] & 60 & 0.6 & 54.2378 & 89.1970 & 93.4000 & 97.2923 & 96.8158 & 94.9367 & 95.8671 \\ [PERSON] & 120 & 0.7 & 48.2023 & 89.7623 & 92.2000 & 95.5069 & 97.0988 & 90.3797 & 93.6189 \\ [PERSON] & 120 & 0.6 & 54.3165 & 88.7264 & 91.0667 & 93.9176 & 96.3669 & 89.5359 & 92.8259 \\ [PERSON] & 30 & 0.7 & 48.5479 & 85.1622 & 91.4667 & 97.1134 & 97.8102 & 90.4641 & 93.9939 \\ [PERSON] & 30 & 0.3 & 53.3554 & 84.7824 & 89.1333 & 95.9313 & 95.1370 & 90.8017 & 92.9188 \\ RMSProp & 60 & 0.3 & 62.9173 & 88.4458 & 93.7333 & 96.3850 & 95.4281 & 96.8776 & 96.1474 \\ RMSProp & 30 & 0.7 & 54.6198 & 87.0119 & 91.2000 & 95.3986 & 92.1900 & 96.6245 & 94.3552 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Key results on dropout and block size impact on AUC. D—dropout, BS—block size. Figure 9: Dropout and block size impact on F1. (**a**)—Adam optimizer; (**b**)—RMSprop—optimizer. Firstly, a larger block size of 120 generally results in shorter training times compared to smaller block sizes of 30 and 60. This can be explained by the fact that a larger block size provides more efficient data utilization, reducing the number of updates per epoch. To better understand the relationship between time and block size, it is worth reviewing some average times by block size:  
* Block size 30: about 1100 s;
* Block size 60: about 900 s;
* Block size 120: about 700 s.  
From these results, we see that increasing the record length from 30 to 120 reduces the average training time by about 37%. However, it is important to note that although a larger block size can help reduce training time, it affects the accuracy of the model. Summarized results of the experiment are provided in Table 14.  
The initial reproduced data exhibit quite high results, especially the accuracy of g1--63.58%, the highest of all other models, and other results: g2 accuracy--87.18%; overall accuracy--92.93%; AUC--96.80%; Precision--96.76%; Recall--93.33%; and F1--95.02%. The article provided limited information on the c23 data level, but the AUC is very high--97.3%. The research results with Adam achieve the highest results among all models, in almost all criteria, except for g1--51.87% and Recall--95.27%. Furthermore, g2 accuracy--88.66%; overall accuracy--97.45%; AUC--97.45%; Precision--97.66%; Recall--95.27%; and F1--96.45%.