# iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
## 4 Experimental Work
### Results  
All video key frames from both datasets [35; 36] were tested on the trained model. Table 3 shows frame-level AUC scores for our DFDC-P results compared to recent detection approaches. The iCaps-Dfake proposed model outperformed other models by an average 12.12% increase in the frame-level AUC.  
Table 4 shows two test results for our proposed model on Celeb-DF. In the first test, an average 18.01% increase is achieved over previous methods by only fine tuning the model, that was pretrained on DFDC-P, for one epoch on Celeb-DF faces thus showing a generalization capability of the proposed model. The second experiment was performed to test the model's results after being trained for more than 50 epochs using the Celeb-DF dataset and produced an average 26.91% increase over others.  
Tables 5 and 6 show the video-level AUC comparison between our iCaps-Dfake model and previous methods. An increase of 3.4% over the previous method on DFDC-P and 20.25% increase on Celeb-DF are reported. Figure 11 sketches the corresponding Receiver Operator Characteristic (ROC) curve for the provided results. It shows that Celeb-DF results are better than DFDC-P results although the detection rate of the former is less than the latter as previously discussed.  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Celeb-DF** \\ \hline Two-stream [49] & 53.8 \\ Meso4 [50] & 54.8 \\ MesoInception & 53.6 \\ HeadPose [39] & 54.6 \\ FWA [31] & 56.9 \\ VA-MLP [52] & 55.0 \\ VA-LogReg & 55.1 \\ Xception-raw [34] & 48.2 \\ Xception-c23 & 65.3 \\ Xception-c40 & 65.5 \\ Multi-task [53] & 54.3 \\ Capsule [61] & 57.5 \\ DSP-FWA [32] & 64.6 \\ [PERSON] et al. [33] & 66.0 \\ FakeSpotter [54] & 66.8 \\ Two-Branch [38] & 73.41 \\
**iCaps-Dfake (fine-tuned)** & 77.1 \\
**iCaps-Dfake (trained)** & **86.0** \\ \hline \end{tabular}
\end{table}
Table 4: Frame-level AUC scores (%) for the proposed iCaps-Dfake model compared to previous detection methods when tested by Celeb-DF.  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **DFDC-P** \\ \hline Two-stream [49] & 61.4 \\ Meso4 [50] & 75.3 \\ Mesolnception & 73.2 \\ HeadPose [39] & 55.9 \\ FWA [31] & 72.7 \\ VA-MLP [52] & 61.9 \\ VA-LogReg & 66.2 \\ Xception-raw [34] & 49.9 \\ Xception-c23 & 72.2 \\ Xception-c40 & 69.7 \\ Multi-task [53] & 53.6 \\ Capsule [61] & 53.3 \\ DSP-FWA [32] & 75.5 \\
**iCaps-Dfake (fine-tuned)** & **76.8** \\ \hline \end{tabular}
\end{table}
Table 3: Frame-level Area-Under Curve (AUC) scores (%) for the proposed iCaps-Dfake model compared to previous detection methods when tested by DFDC-P.  
Table 7 shows that our trained model outperformed the video-level accuracy of a very recently published model with a 3.35% increase when compared on Celeb-DF [35]. As shown in Table 8, the proposed iCaps-Dfake model used an average number of parameters when compared to other methods yet there was over 20% improvement in the AUC. This shows that the increase in the number of parameters over the model presented in [61] was compensated by the huge result improvement in the AUC. Finally, Table 9 shows our performance measures for future comparisons of the proposed model when trained and tested on the two datasets. The high value of f-measure shows that the proposed model is not biased to any of the two classes thus it can easily discriminate between real and fake videos/