# Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
## 4 Proposed Methodology
### Architecture of the Proposed DeepFake Detection Model  
In our proposed model architecture, we relied on the strength of the Xception model. While training, we observed that Xception is a swift model, and it gets trained very fast. When we went through the Efficient Net architecture, we found that the basic block of the Efficient Net model is the MB convolution block. The original MB convolution block consists of a separable convolution layer 2D with ReLu activation function, followed by a depth-wise convolution layer 2D with ReLu activation function, followed by a global averaging pooling layer, followed by a convolution 2D layer for squeeze and expansion. We created the DFN, which consists of 48 layers. These layers are divided into 17 modules. In addition, the architecture is divided into three components: entry flow, middle flow, and exit flow. In the entry flow, the max-pooling layer plays a vital role. The basic block of entry flow is repeated thrice, preceded by two standard convolution layers. Each has a stride of two, which means that the kernel takes the hop of two rows and columns when convolution is performed. In the middle flow, the basic building block is repeated eight times. The output of one block is treated as input when the same block is repeated the next time. In this middle flow, we added three MB convolution blocks which make our architecture more accurate and help in better feature extraction. Further, we added three MB Convolutions in the middle flow of our architecture based upon the experiments performed. Our motive is to create a robust and generalized model architecture without increasing the computational complexity of the model. In exit flow, the basic building block is followed by a global pooling layer and a fully connected layer. The main use and advantage of using a fully connected layer are explained in Section 3.2. Finally, the output of all these layers is passed as an input to the classifier, which finally classifies the image. We used XGBoost as the classifier to classify the image into various classes. First, weights of the Image-Net are used as the initial weights in our proposed model rather than the random weights, which makes our model converge faster and improves its performance. Our model has depth-wise convolution instead of normal convolutions to reduce the computation cost. The numbers of multiplication operations are more in the standard convolution layer; hence it is more computationally expensive  
Figure 3: Flow chart of fully connected layers of the proposed model. and time-consuming. In standard convolution, filters across all input channels and the combination of these values are made in a single step. In contrast, in depth-wise separable convolution, this step is performed in two steps. The first step is depth-wise convolution, which it performs in the filtering stage. It applies convolution to a single input channel, whereas, in standard convolution, convolution is applied to all the channels simultaneously. The convolution operation is nothing but element-wise multiplication and adding them all. The second step is point-wise convolution which performs the combining operation. Point-wise convolution involves the linear combination of each output of the layers. Depth-wise separable convolution is much cheaper (in terms of multiplications) and is computationally efficient. The input image is passed through all the layers, and depth-wise separable convolution operations are applied. Max pooling and global average pooling layers reduce the dimensions and train the model in less time. The two-step method has been used to reduce the multiplication operations and make the model computationally efficient. MB Convolution block has three components. Each of these blocks has residual connections from start to finish. Supposing the first layer has found some good features and there are many more layers ahead of it, all the layers need to enrich those features, extract new features, and carry already found features.