# Deep Learning Methods to Detect Image Falsification
## 4 Experiments and Results
### Improved Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset Level** & **g1 Accuracy** & **g2 Accuracy** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\ \hline “raw”—standard deviation & 0.0113 & 0.0023 & 0.0077 & 0.0102 & 0.0129 & 0.0009 & 0.0062 \\ “raw”—variation coefficient & 1.4258 & 0.2596 & 0.823 & 1.0610 & 1.3523 & 0.099 & 0.6465 \\ “c23”—standard deviation & 0.0291 & 0.0029 & 0.0079 & 0.0049 & 0.0093 & 0.0029 & 0.0040 \\ “c23”—variation coefficient & 3.7488 & 0.3164 & 0.8230 & 0.5056 & 0.9692 & 0.2972 & 0.4178 \\ “c40”—standard deviation & 0.0483 & 0.0073 & 0.0156 & 0.0316 & 0.0148 & 0.0081 & 0.0076 \\ “c40”—variation coefficient & 6.3069 & 0.8753 & 1.772 & 3.5487 & 1.6709 & 0.8286 & 0.8144 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Improved model stability data, based on standard deviations and variation coefficients. the variation coefficients of the \"raw\" (1.0610) and \"c40\" (3.5487) datasets. A smaller variation coefficient indicates that the AUC results of the \"c23\" dataset are less dispersed in comparison to the results of the other datasets. From these comparisons, it can be concluded that the model is more effective and operates more stable than with other datasets with the \"c23\" dataset, especially when it comes to AUC. This indicates that the enhanced model consistently improves upon the performance of the original model, enabling the better identification of manipulated images from real ones. Moreover, the overall accuracy of the improved model was enhanced compared to the results of the replicated model. Unfortunately, with other datasets and model parameters, the model did not achieve better results. Overall, with the improved model and the best parameters found in our experiment, the analyses of the \"c23\", \"raw\", and \"c40\" datasets indicate that the model performs most optimally and stably with the \"c23\" dataset, particularly regarding the AUC measure. With this dataset, the model's effectiveness in discerning real images from forged ones and its overall accuracy is improved. However, the model's performance does not show similar improvements with other datasets and parameters. Comparing standard deviations and coefficients of variation, the \"c23\" dataset consistently exhibits less dispersion and variation in results, underlining its stability and efficiency. In contrast, the \"raw\" and \"c40\" datasets showed larger standard deviations and coefficients of variation for most parameters, highlighting their relatively less stable and consistent performance. The improvements we observed in the model's accuracy for the \"c23\" dataset are a direct result of the specific optimizations we made, which were tailored based on the characteristics and patterns identified during our experimentation phase. However, it's important to note that these optimizations were not necessarily designed to enhance performance on other datasets, such as the raw and \"c40\" datasets. This explains why the accuracy values for these datasets did not show similar improvements. The optimizations that improved performance for the \"c23\" dataset may not be effective for other datasets due to their unique characteristics and data composition. Consequently, the improvements significantly contributed to enhancing the model's performance and stability, particularly in relation to AUC results.