# GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
## 2 Related Work
### Deepfake Detection  
The key point of the deepfake detection pipeline is determining whether a given video is fake or real. With the advancement of DL methods that can create hyper-realistic images, deepfake detection has become an increasingly challenging task. To this end, various authors have proposed deepfake detection techniques that use different approaches, including visual features, biological signals, and frequency information, to name a few [49; 50]. Several deepfake detection methods rely on extracting visual features from manipulated videos. MesoNet [12] is a deepfake detection method that uses CNNs to extract mesoscopic properties and identify deepfakes created with techniques like Deepfake [51] and Face2 Face [39]. [PERSON] et al. [52] proposed a model that uses a combination of VGG-19 and capsule networks to learn complex hierarchical representations to detect various types of forgery, including FaceSwap [42], facial reenactment [39], replay attacks, and AI-generated videos. [PERSON] et al. [13] proposed a model for comparing 3D head poses estimated from all facial landmarks. The method considers splicing synthesized face regions into original images to introduce errors in landmark locations. These landmark location errors can be detected by comparing the head poses estimated from the facial landmarks. [PERSON] and [PERSON] [53] proposed a CNN model for deepfake detection that identifies face-warping artifacts. Their method leverages the limitations of deepfake algorithms that generate face images of lower resolutions, which results in distinctive warping artifacts when the generated images are transformed to match the original faces in the deepfake creation pipeline. By comparing the deepfake face's region with surrounding pixels, resolution inconsistencies caused by face warping can be identified. [PERSON] et al. [54] proposed a technique called Face X-ray that detects the blending boundaries of images and reveals whether an input face image has been manipulated by blending two images from different sources. [PERSON] et al. [55] proposed a virtual-anchor-based approach that robustly extracts the facial trajectory, capturing displacement information. They constructed a network utilizing dual-stream spatial-temporal graph attention and a gated recurrent unit backbone to expose manipulated videos. The proposed method achieves competitive results on the FaceForensics++ dataset, demonstrating its effectiveness in detecting manipulated videos. Overall, these diverse methods of detecting deepfakes based on visual cues demonstrate that subtle visual inconsistencies and artifacts can be used to expose manipulated videos. In addition to visual features, researchers have explored the use of biological signals and low-level features for deepfake detection. [PERSON] [56] proposed a model that combines a CNN and a recurrent neural network (LRCN) to detect deepfake videos by tracking eye blinking using previous temporal knowledge. [PERSON] et al. [57] proposed a modified XceptionNet architecture, which uses visual frames, edge maps, and dense optical flow maps alongside RGB channel data to target low-level features. The architecture isolates deepfakes at the instance and video levels, making the technique effective in detecting deepfakes. [PERSON] et al. [58] proposed pair-wise self-consistency learning with an inconsistency image generator to train a ConvNet [34] that extracts local source features and measures their self-consistency to identify deepfakes. [PERSON] and [PERSON] [29] proposed a facial forensic framework that uses pixel-level color features in the edge region of an image and a 3D-CNN classification model to interpret the extracted color features spatially and temporally for generalized and robust face manipulation detection. [PERSON] et al. [59] proposed a Recurrent convolutional model, whereas [60] proposed a Convolutional Vision Transformer (CViT) model.