# Tampered and Computer-Generated Face Images Identification Based on Deep Learning
## 4 Experimental Results
### Imbalanced Dataset Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **1/100** & **1/50** & **1/25** & **1/10** & **1/5** & **1/1** \\ \hline Real & 200,000 & 100,000 & 50,000 & 20,000 & 10,000 & 2000 \\ Tampered & 2000 & 2000 & 2000 & 2000 & 2000 & 2000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The number of tampered and real images on various settings of the imbalanced dataset. When the proportion of the real images to the tampered images is 1/1 (balanced dataset), all models achieved an AUC value of over 0.8. Moreover, the TFID-XGB and the SE-ResNet-50-XGB models reached a slightly higher performance compared to TFID and SE-ResNet-50. The AUC values became lower when the proportion of the real images to the tampered images increased because TFID and SE-ResNet-50 models focused on the features from the majority class and overlooked features from the minority class. The effect IDP can be observed under the extreme setup when the proportion of the real images to the tampered images was 1/100. The AUC values of the TFID and the SE-ResNet-50 models plummeted to 0.59 and 0.6, respectively. However, the results obtained from ensemble-based models (XGB) were more robust and remained over 0.8 compared to the other extensions of TFID. Among two ensemble-based models, TFID-XGB achieved an AUC value of 0.92, and SE-ResNet-50-XGB reached an AUC value of 0.88 with the highly imbalanced ratio of 1/100. In addition, the cost-sensitive learning approach, which includes TFID-CW and SE-ResNet-50-CW models, also witnessed a high AUC value between 0.76 and 0.88. We noticed that the data-based hybrid models (TFID-SA and SE-ResNet-50-SA) performance decreased gradually as the number of real images increased, and the TFID-SA and SE-ResNet-50-SA reached their lowest AUC value at 0.64 and 0.71, respectively, when the proportion was 1/100. The main reason that led to the poor performance of the data-based approach is that the generated images using the augmentation technique were just the extension of the original images. Thus, it can lead to the overfitting problem [31, 32]. After calculating the AUC value, macro-precision, macro-recall, and macro-f1 are computed. These measurements are usually computed when we want to evaluate the performance of the system on different datasets. Moreover, these measures are invariant with respect to the IDP. Macro-precision and macro-recall are computed by averaging the precision and recall of a classifier on different datasets. Table 6 shows the computed macro-precision, macro-recall, and macro-f1 of 8 different models in different imbalanced dataset settings. The highest macro-f1 value belongs to SE-ResNet-50-XGB model, whereas the proposed TFID-XGB model achieves the macro-f1 value of 0.887. Obtained results confirm that the ensemble-based approach using XGB is the most effective way to deal with the IDP. In the previous section, the ensemble-based extensions of TFID and SE-ResNet-50 models outperformed other approaches because the AUC values always remained over 0.8, even in the most imbalanced scenario. This experiment is conducted to compare these models in terms of computational complexity to check which model requires the lowest testing time and which model demands the highest testing time. The testing time per image of eight different models, including TFID, TFID-CW, TFID-XGB, TFID-SA, SE-ResNet-50, SE-ResNet-50-CW, SE-ResNet-50-XGB, SE-ResNet-50-SA on MANFA dataset are shown in Figure 10 (the proportion of tampered images to real images is 1/10). As shown in Figure 10, the obtained results confirm that the testing time per image of SE-ResNet-50, SE-ResNet-50-SA and SE-ResNet-50-CW models is about 3 s. In addition, SE-ResNet-50-XGB requires 4.2 s per image, which is the longest time among the extensions of SE-ResNet-50 models. In contrast,