  In recent years, the rapid advancement of deepfake technology has
revolutionized content creation, lowering forgery costs while elevating
quality. However, this progress brings forth pressing concerns such as
infringements on individual rights, national security threats, and risks to
public safety. To counter these challenges, various detection methodologies
have emerged, with Vision Transformer (ViT)-based approaches showcasing
superior performance in generality and efficiency. This survey presents a
timely overview of ViT-based deepfake detection models, categorized into
standalone, sequential, and parallel architectures. Furthermore, it succinctly
delineates the structure and characteristics of each model. By analyzing
existing research and addressing future directions, this survey aims to equip
researchers with a nuanced understanding of ViT's pivotal role in deepfake
detection, serving as a valuable reference for both academic and practical
pursuits in this domain.
