Deepfake detection aims to authenticate facial images and videos, which can offer operational and technical support to safeguard personal portrait rights, prevent fake news, and curb online deceit. Early detection technologies are usually based on convolutional neural networks (CNNs) and have achieved promising detection performance. However, there exists a prevalent issue of mediocre generalisation performance. To enhance the overall generality of Deepfake detection, recent research has focused on a deep neural network Transformer by utilizing the self-attention mechanisms. The Transformer can better model long-distance dependency and global receptive fields to capture the image context association and video timing relationships, such that the representation ability of the detectors can be improved. This survey first provides an overview of the research background in this field, followed by an explanation of the common techniques used to generate Deepfake. Then, the existing Transformer-based detection methods are summarized and comparatively evaluated. Finally, the challenges and future research directions of Deepfake detection are discussed.