# Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
## 3 Preliminaries
### Efficient Net Model  
Convolutional Neural Networks (CNNs) work on recurrent learning methods. In the training phase, a CNN randomly assigns weights to all the features, finds the results, and matches them with the actual results to calculate the error. Based on the error, it reassigns or adjusts the weights of all the features. This is continued to obtain good accuracy. Due to this feature, people have thought of increasing the number of layers in the convolutional neural network to achieve higher accuracy. Dropout is used at each layer to train different neurons on different features.  
If all the neurons are trained on the same feature, they will all give the same result for an unseen image. This reduces the accuracy of the model. To avoid this situation, the dropout is tuned as per the requirement. By increasing the number of layers, accuracy is improved to a great extent. Many experiments were performed to find the saturation level of the number of layers of a convolution neural network. It reached a saturation level on 152 layers in the ResNet152 model. By increasing the number of layers, we increase the depth and, thereby, the complexity of the model. As the complexity increases, the time taken to train the model also increases. This leads to the requirement of high computational resources like GPU, memory, SSD, etc.  
To reduce the complexity and the need for computational resources, researchers found a solution in terms of the horizontal scaling of the model. In 2019, [PERSON] and [PERSON] proposed a new model, created by scaling up the convolutional neural network on all three aspects of width, depth, and resolution. This model was named the Efficient Net Model [28].  
They determined that although it is very critical to balance all three aspects (width, depth, and resolution) in a particular model, if we scale up our model on all three aspects while maintaining balance among all three aspects, we can achieve high accuracy with less computational resources. This model is 8.4\(\times\) smaller and 6.1\(\times\) faster on inference than the best existing convolutional neural networks.  
They proved their high accuracy against various other existing models. Through extensive research, they derived an optimal formula with the following coefficients to maintain a balance among all three aspects: Depth = 1.20, Width = 1.10, Resolution = 1.15. This means that to scale up the CNN Model, the depth of layers must increase by 20%, width by 10%, and resolution by 15% to achieve the highest efficiency possible while expanding the implementation and improving the accuracy of the CNN model. This model is designed for the segmentation of images (Table 1). The original output layer consists of 1000 outputs. We added a linearly fully connected layer with the ReLu activation function to get the binary output. Two GPUs were required for training.  
The Efficient Net model uses the method of compound scaling, which uses a compound coefficient \(\varphi\) for scaling all the parameters like network width, depth, and resolution uniformly in a principled way:  
\[\text{Depth}:\ d=\alpha^{\phi} \tag{1}\] \[\text{Width}:\ w=\beta^{\phi}\] (2) \[\text{Resolution}:\ r=\gamma^{\phi}\] (3) \[s.t.\alpha*\beta^{2}*\gamma^{2}\approx 2\] \[\alpha\geq 1,\beta\geq 1,\gamma\geq 1\]  
where \(\alpha\), \(\beta\), and \(\gamma\) are constants that can be determined by a small grid search. Intuitively, \(\phi\) is a user-specified coefficient. They used this in different versions of the Efficient model from B0 to B7. This has increased the complexity of the model. Hence B7 is the highest complexity model and B0 is the minimum complexity version of the model.