# DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer
## 2 Related Work  
A brief description of recent advancements in deepfake analysis domain is provided here. **DeepFake Generation. ** Although early deepfake generation techniques were mostly based on traditional vision and voice impersonation methods [3; 27; 28], most recent techniques benefit from the unique generation capabilities of GANs. For instance, [PERSON] et al. [29], and [PERSON] et al. [30] utilized cycle-consistent GANs to generate deepfakes such that it maintains the facial expressions of the target while swapping the identities of source and target. Furthermore, [PERSON] et al. [31] presented identity-guided conditional CycleGAN to convert low-resolution facial images to high-resolution images. Similarly, [PERSON] et al. [32] introduced a deep video portraits method was introduced to transfer both facial expression and 3D poses of the source image into the target image. Moreover, [PERSON] et al. [33] presented a high-quality face replacement approach through FaceShifter that exploits a learning method based on heuristic error acknowledging refinement network. As the main scope of this study is on deepfake detection techniques, more interested readers are referred to [8; 9] for more detailed information on state-of-the-art deepfake generation techniques. **Deepfake Detection. ** While the deepfake detection task has been studied from different perspectives, this study mainly explores the AI-driven deepfake detection approaches. Given the importance and huge threat of deepfake technologies, a large body of work is devoted to devising high-performance and resilient detection technologies. While early-stage detection techniques mainly focused on handcrafted features, i.e., blinking inconsistencies [12], biological signals [15], and unrealistic details [34], more recent techniques are developed using advanced deep learning networks. For example, [PERSON] et al. [35] introduced _MesoNet_ as a deepfake detection algorithm that is composed of a shallow convolutional network and intermediate level of features. A detection approach based on auto-encoder architecture and transfer learning, _forensictransfer_, was presented by [PERSON] et al. [36]. Similarly, _Capsule-Forensics_ architecture was introduced by [PERSON] et al. [16] for better detection of AI-generated images and videos. Furthermore, an ensemble learning approach was employed to improve deepfake detection composite model by [PERSON] et al. [37]. Additionally, [PERSON] et al. [38] proposed a detection approach based on sequential temporal analysis and convolutional long short-term memory networks. [PERSON] et al. [39] evaluated the cross-dataset generalization capability of their detection model, which was trained over ProGAN and tested on other datasets. [PERSON] et al. [40] proposed a multi-modal approach composed of audio and video modalities to tackle deepfake detection tasks. Furthermore, [PERSON] al. [14] introduced a hierarchical classification approach that can recognize deepfakes at three different levels, including manipulated images, retouched from GAN-based images, and specific GAN architecture. Other researchers have investigated deepfake detection tasks from a fine-grained visual classification point of view, specifically attention-based techniques. For instance, [PERSON] et al. [13] proposed a deepfake detection method from a fine-grained visual classification angle that is built using an auto-encoder architecture. Furthermore, [PERSON] and [PERSON] [18] have presented an attention-based deepfake detection approach utilizing two different modules, i.e., Face close-up and Face Shut-off, to force the model to extract more discriminative information from other parts of the facial region. [PERSON] et al. [41] presented a progressive transfer learning algorithm to tackle face spoofing attacks using only a limited number of training samples. The presented face anti-spoofing method benefits from a temporal consistency constraint to verify the reliability of pseudo labels of selected data.