# Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
## 5 Experiments  
### Dataset  
The dataset plays a critical role while developing a deep learning model. Many times, results are based on the dataset selected. We selected the DFDC (DeepFake Detection Challenge) dataset [31]. Its size is approximately 470 GB. This dataset is taken from Kaggle Competition, provided by Google.  
It is a generalized dataset but images are not provided for certain events like eye blinking, swapped face, lip movement, etc. As a result, the model is not able to predict the desired result. Performing any simulation using this dataset requires us to create and refine different rules to pre-process the data in order to enhance the performance of trained model.  
We divided this dataset into training, validation, and testing sets. This dataset contains both real and fake videos. Videos are converted into frames and frames having faces were selected to provide input to the model shown in Figure 6. After converting all the videos into frames and detecting faces from all the frames, we used 65,234 real images and 68,258 fake images for training our model. For validating, 5876 real images and 5698 fake images were used. Testing was performed on 9785 real images and 9542 fake images.  
Figure 5: Flow chart of deep fake detection with a sample video.  
The statistics of the DFDC dataset that we used for training, validation, and testing of our model are shown in Table 3, specifically the number of frames having faces. Other frames (not having faces) extracted from the videos were not used while training or testing our model. After removing the frames without faces, we found 65,234 real and 68,258 fake images for validating our model, 5876 real and 5698 fake images for training our model, and 9785 real and 9542 fake images for testing our model. We kept a large amount of data for training our model so that it learns maximum features, as well as some unseen data for testing the performance of our model.  
To visualize the dataset and understand its characteristics, for feature distribution, we used t-SNE, t-distributed stochastic neighbor embedding plot. It has been used to map multi-dimensional data (images) in two dimensions. It is a nonlinear algorithm which is very useful for exploring high dimensional data and dimensional reduction.  
Figure 7 shows the visualization of our dataset by giving each data point (image) a location in a two-dimensional map. It shows the real and fake images. The value 0 is used for fake images and 1 is used to represent real images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Dataset** & **DFDC (DeepFake Detection Challenge)** \\ \hline \hline  & **Size** & **470 GB** \\ \hline \multirow{2}{*}{Training} & Frames having faces (Real) & 65,234 \\  & Frames having faces (Fake) & 68,258 \\ \multirow{2}{*}{Validation} & Frames having faces (Real) & 5876 \\  & Frames having faces (Fake) & 5698 \\ \multirow{2}{*}{Testing} & Frames having faces (Real) & 9785 \\ \multirow{2}{*}{Training} & Frames having faces (Fake) & 9542 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset statistics.  
Figure 6: Sample images from the DFDC dataset.