# Deep Learning Methods to Detect Image Falsification
## 4 Experiments and Results
### Improved Model
This table presents the reproduced results, reported results, the average of the results obtained from the cross-validation with the best parameters found during the experiment, and the average of the results obtained from the cross-validation with the improved model. The improved model's \"raw\" variant achieved very similar results to the reproduced model's \"raw\" and the best parameter model's \"raw\" results. This indicated that the improved model with the \"raw\" dataset had slightly lower performance indicators, such as g1 accuracy (79.78%), g2 accuracy (90.70%), overall accuracy (94.54%), AUC (96.92%), precision (95.96%), recall (98.11%), and F1 (97.02%). Compared to the reproduced model, AUC was 1% lower, with the reported model AUC 3% lower, and with the best parameter model AUC 0.5% lower. Secondly, the improved model's \"c23\" variant achieved better results than the reproduced model in almost all criteria. g1 accuracy increased from 63.58% to 77.77%, g2 accuracy increased from 87.18% to 92.40%, overall accuracy increased from 92.93% to 96.17%, AUC increased from 96.80% to 98.39%, precision did not change, recall increased from 93.33% to 98.71%, and F1 increased from 95.01% to 97.72%. Compared with the data reported in the article, AUC improved by 1.08%, compared to the best parameter model by 0.36%, and compared to the reproduced model by 1.58%. Finally, the improved model's \"c40\" variant showed similar results to the reproduced model. g1 accuracy decreased from 81.53% to 76.71%, but g2 accuracy increased from 82.44% to 83.46%, overall accuracy decreased from 91.28% to 88.54%, AUC decreased from 93.71% to 89.19%, precision decreased from 93.53% to 89.14%, recall increased from 95.53% to 98.29%, and F1 decreased from 94.52% to 93.48%. Compared to the best parameter model, almost all results were about 1% lower. In summary, the improved model showed the best AUC results with the \"c23\" dataset among all models and the best results for other parameters but showed slightly lower results with other datasets. To ensure or verify that the results obtained with the improved model were consistent, Table 18 presents the standard deviations and variation coefficients of the cross-validation results. When comparing the standard deviations of different datasets, the \"raw\" dataset exhibits smaller standard deviation sizes for g2 accuracy, overall accuracy, AUC, and F1, in comparison to the \"c40\" dataset. It also demonstrates smaller standard deviations for g2 and overall accuracy when compared to the \"c23\" dataset, albeit with a negligible difference. Conversely, the \"c40\" dataset displays larger standard deviation sizes for all parameters when compared to the \"raw\" and \"c23\" datasets. Overall, the standard deviations are minimal for all datasets, but the noteworthy finding is that the AUC and F1 standard deviations are the smallest with the \"c23\" dataset. Regarding the coefficient of variation in different datasets, it was observed that the \"c40\" dataset exhibits larger coefficients of variation for g1 and g2 accuracy, overall accuracy, AUC, precision, recall, and F1 when compared to the \"c23\" and \"raw\" datasets. The \"raw\" dataset demonstrates the smallest coefficients of variation, except for AUC, precision, and F1 results. The \"c23\" dataset showcases the best AUC, precision, and F1 results. The AUC standard deviation (0.0049) of the \"c23\" dataset is smaller than the standard deviations of the \"raw\" (0.0102) and \"c40\" (0.0316) datasets. A smaller standard deviation indicates that the AUC results of the \"c23\" dataset are more concentrated around the mean, suggesting a higher level of stability and reduced result variability for the model. Furthermore, the AUC variation coefficient (0.5056) of the \"c23\" dataset is smaller than