# iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
## 2 Literature Review
[47] detected the temporal pattern of blinking in early created deepfakes by using a Long-term Recurrent Convolutional Network (LRCN) since the rate of fake eyes' blink was less than that of the normal. Another physiological-based detector is Gaze Tracking [48] where the authors trained a simple CNN on the extracted 3D eye and gaze features. The problem with the physiological-based methods is that it can be easily invaded if the creator added a simple component, a discriminator in GANs, that searches for these specific biological signals. Undirected approaches detect deepfakes by applying deep learning models to extract features and find patterns thus classifying real and fake. The models can be then generalized to detect deepfakes created by any method not just the artifacts left by a specific tool. In Two-Stream [49], [PERSON] et al. combined the feature extraction models of GoogleLeNet and a patch-based triplet network to detect face artifacts and enhance the local noise residuals. MesoNet [50] targets unseen images properties by the application of two variants of CNN; Meso4 that uses conventional convolutional layers and MesoInception4 which is based on Inception modules [51]. Artifacts in eyes, teeth and facial lining of fake faces were addressed by VisualArtifacts (VA) [52]. They created two variants; a multilayer feedforward neural network (VA-MLP) and a logistic regression model (VA-LogReg). Another model, Multi-task, was created by [53] used a CNN to perform a multi-task learning problem by classifying fake videos and specifying manipulated areas. In FakeSpotter [54], [PERSON] et al. managed to overcome noise and distortions by monitoring the pattern of each layer's neuron activation of a face recognition network to seize the fine features that could help in detecting fakes. They tested their model on FF++, DFDC [55] and Celeb-DF [35] and produced comparable results. In DeepfakeStack [56], the authors followed a Greedy Layer-wise Pretraining technique to train seven deep learning models (base-leaners) with ImageNet weights which were computationally very expensive. They used Stacking Ensemble (SE) and trained a CNN as their meta-learner to enhance the output of the base-learners. The authors trained and tested their model on FF++ dataset. Capsule networks are also used, as an undirected approach, in different deepfake detection techniques. First introduced by [PERSON] et al. [57], they have recently shown exceptional capabilities in feature reduction and representation [58; 59]. [PERSON] et al. were involved in two approaches [60; 61] where in the first they created a network consisting of three primary and two output capsules that were fed latent features extracted by VGG-19 [62], they finally added a statistical pooling layer to deal with forgery detection. Their second approach also used capsules to segment manipulated regions. Both models were tested and trained on FF++ which proved a very high detection rate [35]. The proposed model combines both artifacts and undirected approaches as it exploits environmental artifacts using LBP to perform the texture analysis and use a deep learning-based model HRNet to automatically detect informative multi-resolution feature representations. Together with the capsule network as a classifier it outperformed previous methods and achieved generalization.