Nowadays forensics methods have shown remarkable progress in detecting maliciously crafted fake images. However, without exception, the training process of deepfake detection models requires a large number of facial images. These models are usually unsuitable for real world applications because of their overlarge size and inferiority in speed. Thus, performing dataefficient deepfake detection is of great importance. In this paper, we propose a contrastive distillation method that maximizes the lower bound of mutual information between the teacher and the student to further improve student’s accuracy in a datalimited setting. We observe that models performing deepfake detection, different from other image classification tasks, have shown high robustness when there is a drop in data amount. The proposed knowledge transfer approach is of superior performance compared with vanilla few samples training baseline and other SOTA knowledge transfer methods. We believe we are the first to perform few-sample knowledge distillation on deepfake detection.