# Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
A sample of fake images created from such apps is shown in Figure 1. If shown randomly, it is difficult to differentiate between the real and fake images. The fake images can be easily generated by anyone, irrespective of age or knowledge. deepfake detection has become an emerging area of research due to the above reasons. Moreover, it is a challenging and unsolved problem to date. Deepfake allows for the creation of fake videos similar to realistic videos by swapping the complete face of one person with another, or sometimes just by manipulating the eye movement, lip movement, and expression to simulate some other context. In 2018, a fake video of [PERSON] (former president of the US) was released by BuzzFeed in which he was talking about some subject [4]. That deepfake video was created using FakeApp [5] software. It raised many concerns about spreading the wrong information on social media, impersonation, identity theft, etc. It is becoming more threatening when fake videos of our leaders are created to spread rumors, to spread violence, and other falsification purposes [6,7]. It can also be used to create fake satellite images of Earth, to create objects which are not at all present in reality, which can be used by attackers to mislead whole troops of military and analysts, etc., to misguide them to a place where they can be easily attacked or killed [8]. The number of deepfake videos doubles every eight months, according to available statistics. Detecting and removing fake data on the internet can prevent misinformation, rumors, and fake pornography from spreading if we cannot prevent its creation [9]. To achieve this, our detection methods must be robust, generalized, fast, and accurate enough to detect fake data and punish those who are generating and misusing such data. The ResNet 152 [10] and Xception [11] models are performing well in the field of object recognition, as reported in the literature. In addition, several deep learning methods have been successfully applied in several other domains [12, 13, 14, 15]. The proposed model was trained on a large DFDC dataset [16] with a motive to create a robust, generalized, and more accurate model. The DFDC dataset has no data leaks, and our results are not dependent on a particular feature of a face like teeth, smile, eye, lip, etc. We considered all the features of a face, including a complete face swap. We also implemented various image augmentation techniques while training our model to improve the performance gains. Figure 1 shows the abstract view of the deepfake creation and detection model. The first block shows the creation of fake images, manipulated using various techniques. The second block shows the set of real and fake images. It can be considered a dataset, which contains both real as well as fake images. The last block shows the compact view of the  
Figure 1: High Level view of deepfake creation and detection model. detection process. Frames are extracted from videos, and each face is detected from the frames. Then these faces are provided as input to the detection model, where predictions of real and fake videos are made. The main contributions of this work are as follows. 1. We propose a new model architecture, which consists of a linear stack of separable convolution 2D, max-pooling layers with XGBoost as the classifier and modification Swish as an activation function. 2. Rather than focusing on any single facial manipulation technique, we focused on generating a robust, scalable, and generalizable model for deepfake video detection by training the model on an augmented and generalized dataset. 3. The proposed model outperforms on deep fake dataset with less training and validation loss. The rest of this paper is organized as follows. Sections 2 and 3 detail related work and preliminaries of CNN (Convolution Neural Network). Section 4 introduces the proposed model. Section 5 presents experimental details and results of the proposed architecture on the DFDC dataset. Finally, Section 6 gives the conclusions and future scope.