  Deepfake is the manipulated video made with a generative deep learning
technique such as Generative Adversarial Networks (GANs) or Auto Encoder that
anyone can utilize. Recently, with the increase of Deepfake videos, some
classifiers consisting of the convolutional neural network that can distinguish
fake videos as well as deepfake datasets have been actively created. However,
the previous studies based on the CNN structure have the problem of not only
overfitting, but also considerable misjudging fake video as real ones. In this
paper, we propose a Vision Transformer model with distillation methodology for
detecting fake videos. We design that a CNN features and patch-based
positioning model learns to interact with all positions to find the artifact
region for solving false negative problem. Through comparative analysis on
Deepfake Detection (DFDC) Dataset, we verify that the proposed scheme with
patch embedding as input outperforms the state-of-the-art using the combined
CNN features. Without ensemble technique, our model obtains 0.978 of AUC and
91.9 of f1 score, while previous SOTA model yields 0.972 of AUC and 90.6 of f1
score on the same condition.
