# Deep Learning Methods to Detect Image Falsification
## 4 Experiments and Results
### Improved Model  
Based on the results obtained in the previous section, a new improved model version is presented in Figure 11. When creating this model, the goal was to improve the AUC results and accuracy for the \"c23\" data level, compared to the results reported in the article or during the experiment. The \"c23\" data level is also important because these data are subject to data compression, which is often encountered on the internet; thus, this data could be more important considering the relevance of the models' usage. AUC and accuracy are the most important indicators of model effectiveness in distinguishing true images from fake ones. The main differences between the two models are the first dropout rate, the number of GRU hidden neurons, the number of dropout layers, and the configuration of linear layers and activation functions. The improved model has a smaller number of GRU hidden neurons and additional linear and ReLU6 layers, whereas LRNet has a higher first dropout rate and one additional dropout layer. When comparing the two models, the improved model and LRNet, several key differences in their structure are observed. First, the improved model uses a smaller first dropout rate--0.05--compared to LRNet's of 0.25. Second, the GRU hidden neuron count is smaller in the improved model--32--compared to LRNet's 64. In addition, the improved model has one dropout layer with a 0.75 dropout rate, whereas LRNet has two dropout layers with a 0.5 dropout rate. The configuration of linear layers and activation functions is also different. The improved model has three linear layers with ReLU6 activation functions, whereas LRNet has two linear layers with ReLU activation functions. Additionally, the number of neurons in the linear layers of the improved model is smaller since the output of the GRU layer is smaller. The linear layers of the improved model are: 64 \(\times\) 64, 64 \(\times\) 64, 64 \(\times\) 2, whereas LRNet has 128 \(\times\) 64, 64 \(\times\) 2. Finally, both models have a Softmax output layer with a single output. The improved model is more complex, with additional linear layers and ReLU6 activation functions, whereas LRNet has a higher first dropout layer coefficient and one additional dropout layer. The model was trained using previous training data but with 5-fold cross-validation to further verify training errors and ensure that the model consistently achieved better results. However, the testing data remained the same as reported in the article. The cross-validation results for the improved model are shown in Table 17.  
Figure 11: (**a**)—improved model; (**b**)—reported model. Layers in orange—changed from the original model, layers in green—new layers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dataset Level** & **g1 Accuracy** & **g2 Accuracy** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline Reproduced—raw” & 86.3806 & 91.7724 & 95.7143 & 97.8705 & 96.9965 & 98.0357 & 97.5133 \\ Reproduced—”c23” & 63.5757 & 87.1847 & 92.9333 & 96.8073 & 96.7629 & 93.3333 & 95.0172 \\ Reproduced—’c40” & 81.5299 & 82.444 & 91.2857 & 93.7111 & 93.5315 & 95.5357 & 94.523 \\ Reported—”raw” & 83.4 & 98.3 & 99.7 & 99.9 & - & - & - \\ Reported—”c23” & - & - & - & 97.3 & - & - & - \\ Reported—”c40” & 80.4 & 85.2 & 91.2 & 95.7 & - & - & - \\ Best parameters—raw” & 21.7164 & 90.5671 & 95.3142 & 97.4330 & 96.7902 & 97.9285 & 97.3553 \\ Best parameters—’c23” & 21.7164 & 91.5485 & 96.4285 & 98.0241 & 97.4188 & 98.1428 & 97.7770 \\ Best parameters—’c40” & 32.6412 & 82.9144 & 89.6571 & 90.6779 & 90.8831 & 97.25 & 93.9395 \\ Improved model—”raw” & 79.7798 & 90.7014 & 94.5428 & 96.9191 & 95.9633 & 98.1071 & 97.0193 \\ Improved model—”c23” & 77.7686 & 92.4029 & 96.1714 & 98.3896 & 96.7534 & 98.7142 & 97.7213 \\ Improved model—”c40” & 76.7089 & 83.4552 & 88.5428 & 89.1908 & 89.142 & 98.2857 & 93.4827 \\ \hline \end{tabular}
\end{table} 
Table 17: Comparison between different models.