# iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
## 4 Experimental Work
### Dataset  
Many benchmark datasets were recently published to support the research community's race against this drastic phenomenon. Companies like Google and Facebook have shown great interest in the deepfake detection domain as both created datasets (DFD and DFDC) to address this problem. Also, Facebook created a Kaggle competition awarding one million dollars to competitors creating the detection algorithms with the highest scores on the DFDC-P dataset [71; 72]. Table 1 shows a summary of the most used datasets addressing this domain where the visual manipulations are precisely face replacements and only two of them included additional audio manipulations. In [35], it was proven that Celeb-DF and DFDC-P are very challenging datasets based on calculating the average AUC metric when compared to other available datasets using Two-Stream [49], MesoNet [50], HeadPose [39], FWA [31], VA [52], Xception [34], Multi-Task [53], Capsule [61] and DSP-FWA [32] detection methods. This have led us to focus our experiments on those two reliable benchmarks on which multiple trials were performed until the best results were achieved. DFDC-P [36] is the main dataset used to train the proposed model as it contains many extremely low-quality videos which makes it a particularly challenging dataset. It has videos for 60 different actors of which 40 were used for training and the rest for validation. The videos were created in different lightening conditions, head poses and backgrounds by actors varying in skin tone, gender and age. The distribution of gender is 74% females and 26% males having a race split of 68% Caucasian, 20% African-American, 9% east-Asian and 3% south-Asian. Different augmentations were applied on random videos such as reducing the resolution to quarter of its original, reducing the frame per second (FPS) to 15 and reducing the overall video quality. The fake videos are manipulated by two different creation methods thus improving the model's ability to generalize on unseen methods. Figure 9 shows different samples generated from the dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Real**} & \multicolumn{2}{c}{**Fake**} & \multicolumn{2}{c}{**Manipulation**} & \multirow{2}{*}{**Release Date**} \\ \cline{2-2} \cline{5-7}  & **Video** & **Frame** & **Video** & **Frame** & **Visual** & **Audio** & \\ \hline UADFV [39] & 49 & 17.3 & 49 & 17.3k & ✓ & \(\times\) & 2018.11 \\ DF-TIMIT-LQ [73] & 320 & 34.0k & 320 & 34.0k & ✓ & ✓ & 2018.12 \\ DF-TIMIT-HQ & 320 & 34.0k & 320 & 34.0k & & & \\ FF-DF [34] & 1000 & 509.9k & 1000 & 509.9k & ✓ & \(\times\) & 2019.01 \\ DFD [71] & 363 & 315.4k & 3068 & 2242.7k & ✓ & \(\times\) & 2019.09 \\ DFDC-P [36] & 1131 & 488.4k & 4113 & 1783.3k & ✓ & ✓ & 2019.10 \\ Celeb-DF [35] & 590 & 225.4k & 5639 & 2116.8k & ✓ & \(\times\) & 2019.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets Summary. Figure 9: Samples from DeepFakeDetectionChallenge-Preview (DFDC-P) dataset where green boxes are real faces and red are fake. Celeb-DF [35] was used to verify the generalization ability of our model. This dataset was created using real YouTube videos for 62 different actors, 49 of which were used for training and the rest for validation. Also, the dataset includes additional 300 YouTube real videos of random people some of which are included in the test and the remaining in the training set. The fake manipulations are done using an improved deepfake synthesis algorithm [20] that played the main role in enhancing the visual quality as shown in Figure 10. The creation algorithm applied dealt with many artifacts that existed in previous datasets thus making it more challenging.