# Real-Time Advanced Computational Intelligence for Deep Fake Video Detection
## 2 Literature Review  
This section provides a literature study based on the merit and demerit of the methods used by various researchers. [PERSON] et al. [17] used YOLO-CNN-XGBOOST, where YOLO (you only look once) is the face detector used to extract the faces from the video frames, InceptionResNetV2 CNN is used to extract the features from the detected faces, and XGBoost is used as the classifier on the top of the CNN network to classify the video as fake or real. They used the CelebDF-FaceForencicks++ merged dataset for training and testing their model and attained an accuracy of 90.73%. Their dataset consists of 2848 training and 518 testing data selected from CelebDF and FaceForensics++. Their dataset is not large enough to test and train a deep neural network. [PERSON] et al. [18] implemented a two-phase deep learning technique for the detection of deepfake images. In the first phase, they extracted features based on the popular CFFN (Common Fake Feature Network). They used the Siamese network architecture presented in [19]. CFFN uses multiple units in which each unit has multiple blocks that are used to increase the representative capability of the fake images. Discriminative features between the real image and fake images are extracted as part of the learning process of the model. Then, the features are fed as inputs to the second phase, which is CNN combined with the last layer of CFFN. The accuracy claimed by the researchers is 90.04%. [PERSON] et al. [20] mainly focused on four types of face manipulation techniques. (i) Entire Face Synthesis: In this method, the complete face of the target image replaces the complete face of the source image. (ii) Attribute Manipulation: In this technique, some attributes like hairs, specs, ageing, etc., are replaced in the source image by that of the target image. (iii) Expression Swap: in this technique, expressions of the image like a smile or eye features are manipulated to match with those of the target image. (iv) Identity Swap: In this technique, the identity of the source image is replaced with that of the target image. They experimented on already existing models, which worked well on the seen data; however, a model was not generalized for complex data. They also mentioned that although the models perform well on specific datasets, like UADFV and FaceForensics++ [21], their accuracy suffers with much error when applied to a generalized dataset like the DFDC dataset. [PERSON] et al. [22] defined deepfake as \"Believable media that is created by a branch of machine learning (Deep Neural Network)\". They have categorized human visuals into Reenactment and Replacement categories. In Reenactment, a few attributes are manipulated, like the pose of a person, head position, mouth position for facial expression, gaze of a person that includes eye position, eyelid position, etc. In Replacement, the complete face is swapped or transposed from the source image to the target image. They also focused on various technology drawbacks related to deepfake, and the current status of attacker and defender games, to give a deep insight to the readers and guide them in future research. [PERSON] and [PERSON] [23] used a RNN (Recurrent Neural Network) model for deepfake video detection. According to the researchers, a simple pipeline architecture can work well and can achieve competitive results. They used LSTM for this purpose. As LSTM is designed for sequential data, this model does not work well on unseen data. Moreover, this model also takes much time to converge for high-resolution images, and thus a long time to predict the results. In [4], [PERSON] et al. described the procedure for creating a fake video. They proved that after training a model for a large dataset, the model maps the lip shape and lip movement as per the change in audio. They showed lip-sync merged with the new audio with such perfection that it was difficult for the human eye to recognize that it is a fake video. They created a fake video of [PERSON] (former President of the USA).