# Deep Learning Methods to Detect Image Falsification
## 4 Experiments and Results
### Model Parameter Experiment
RMSprop with the best parameters also exhibits high indicators, but overall has inferior results compared to the research results of Adam: g1 accuracy--57.31%; g2 accuracy--86.62%; overall accuracy--92.86%; AUC--96.82%; Precision--94.76%; Recall--96.20%; and F1--95.47%. The main parameters are overall accuracy, AUC, and F1, and, during the research, the best results with the Adam optimizer surpass the AUC results indicated in the article by 0.1%. Compared to the reproduced model results, AUC is improved by 0.6%. Additionally, compared to the reproduced model results, g2 is improved by 1.5%, overall accuracy by 1.7%, and F1 by 1.4%. However, when comparing the reproduced model results with the best RMSprop research results, RMSprop results are slightly inferior or almost identical, except for the F1 result, which is better by 0.4%. Based on this analysis, the best Adam research results indicate that there are parameters for this model that produce better results, and these parameters are selected for further analysis because they exhibit the highest results in almost all measurement indicators. These best parameters are:  
* Learning rate--0.005;
* Dropout rate--0.7;
* Sequence length--60;
* Optimizer--Adam. Therefore, by choosing these new parameters, the model is trained using previous training data, but with 5-fold cross-validation to further verify that the training is consistently better than the results reported in the article. The testing data are left the same as specified in the article. The cross-validation training results of the model are shown in Table 15. \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 Reproduced results & 63.5756 & 87.1847 & 92.9333 & 96.8073 & 96.7629 & 93.3333 & 95.0171 \\ Reported results & - & - & - & 97.3 & - & - & - \\ Adam—best experiment results & 51.8731 & 88.6591 & 94.6 & 97.453 & 97.6644 & 95.2743 & 96.4545 \\ RMSprop—best experiment results & 57.3184 & 86.6276 & 92.8667 & 96.8271 & 94.7631 & 96.2025 & 95.4774 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of reported, reproduced, and best results of Adam and RMSprop in the experiment with c23 dataset. In this table, the reproduced results of the article, the results reported in the article, and the average results obtained by performing a 5-fold cross-validation with the best parameters found during the experiment are presented. The best parameters \"raw\" results show a g1 accuracy of 21.71%, a g2 accuracy of 90.56%, an overall accuracy of 95.31%, an AUC of 97.43%, a precision of 96.79%, a recall of 97.93%, and an F1 score of 97.36%. The results show that none of the parameters are improved compared to the reproduced or reported results with these parameters and \"raw\" dataset. Compared to the reported results, the accuracy is about 5% lower, and the AUC is about 2% lower, with g1 being completely poor and practically not working correctly. With the \"c23\" dataset, the results show a g1 accuracy of 21.71%, a g2 accuracy of 91.54%, an overall accuracy of 96.43%, an AUC of 98.02%, a precision of 97.42%, a recall of 98.14%, and an F1 score of 97.78%. Compared to the AUC result reported in the article, it was improved by 0.7%, and the other results were not reported in the article for this dataset level. Compared to the reproduced model results, all results are improved except for g1. The accuracy is improved by 3.5%, the AUC by 1.2%, and the F1 score by 2.7%. Finally, with the \"c40\" dataset, the results show a g1 accuracy of 32.64%, a g2 accuracy of 82.91%, an overall accuracy of 89.66%, an AUC of 90.68%, a precision of 90.88%, a recall of 97.25%, and an F1 score of 93.94%. Compared to the \"c40\" results reported in the article, all results are slightly lower by a few percentage points, e.g., the accuracy is 1.7% lower, and the AUC is 5% lower.