# GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
## 2 Related Work
### Deepfake Generation  
Deepfake videos are created using advanced DL methods, such as GANs, VAEs, and DMs. The term deepfake was first introduced in 2017 on the social media platform Reddit by a user called [PERSON] to showcase a DL technique to generate deepfake videos [6]. The videos and the accompanying code garnered significant attention, and soon people started to explore other ways to create a hyper-realistic video. Some widely used DL techniques to create deepfakes include face synthesis, face reenactment, face replacement, identity swapping, attribute manipulation, and expression swapping, to name a few. GANs are a type of generative model (GM) that consists of two neural networks (NNs); a generator \(G\), which takes in random noise and produces synthetic data; and a discriminator \(D\), which determines whether the input sample is real or fake. Another NN used to create deepfakes is the VAE, which consists of an Encoder and a Decoder NN. The Encoder encodes the input image into a lower-dimensional latent space, and the Decoder reconstructs an image from that latent space. Convolutional Neural Networks (CNNs) [36; 37] are also used for face synthesis as they can learn the mapping between a face image and its attributes, such as its facial expression, age, and gender.  
Face synthesis [38] is a method for synthesizing desired non-existent face images based on a given input image. In face synthesis, a generator \(G\) generates a face image, and a discriminator \(D\) discriminates whether the sample is real or fake. Face reenactment [39; 40; 41] is a face synthesis method that transfers source face attributes to a target face while preserving the appearance and identity of the target face's features. Face2 Face [39] is a real-time face reenactment technique that animates the facial expressions of the target video by using a source actor, generating a manipulated output video. Some other examples of face synthesis methods include frontal view synthesis, changing the facial pose of an input image, altering facial attributes, and aging the face to create diverse and realistic results. Face swapping [42; 43; 44] is the process of replacing a person's face in an image or video with the face of another person to create a non-existent realistic face.  
Several types of GANs have been proposed for face synthesis, including Progressive GANs [45], Wasserstein GANs [46], and Style-Based GANs [20]. Progressive GANs allow for the generation of high-resolution images by gradually increasing the resolution of their generated images throughout the training process. Wasserstein GANs improve the stability of the GAN training process by using a different loss function. Style-Based GANs allow for the control of certain styled aspects of the generated faces, such as facial expression and hair style. Conditional GANs [21], also known as supervised GANs, use labeled data to generate facial semantics. Paired image-to-image translation GANs [47; 48] are a type of conditional GAN that can translate an input image from one domain to another when given input-output pairs of images as training data. Pix2 Pix is one example of a paired image-to-image translation GAN.  
In conclusion, deepfake generation leverages a diverse range of deep learning methods, including GAN architectures (e.g., Progressive, Wasserstein, and Style-Based GANs), Transformers, VAEs, and CNNs. These methods are applied to create hyper-realistic images and videos through techniques such as face synthesis, reenactment, and face swapping. These rapid advancements and the diversity of approaches pose significant and evolving challenges for deepfake detection.