  Face forgery by deepfake is widely spread over the internet and this raises
severe societal concerns. In this paper, we propose a novel video transformer
with incremental learning for detecting deepfake videos. To better align the
input face images, we use a 3D face reconstruction method to generate UV
texture from a single input face image. The aligned face image can also provide
pose, eyes blink and mouth movement information that cannot be perceived in the
UV texture image, so we use both face images and their UV texture maps to
extract the image features. We present an incremental learning strategy to
fine-tune the proposed model on a smaller amount of data and achieve better
deepfake detection performance. The comprehensive experiments on various public
deepfake datasets demonstrate that the proposed video transformer model with
incremental learning achieves state-of-the-art performance in the deepfake
video detection task with enhanced feature learning from the sequenced data.
