  Face deepfake detection has seen impressive results recently. Nearly all
existing deep learning techniques for face deepfake detection are fully
supervised and require labels during training. In this paper, we design a novel
deepfake detection method via unsupervised contrastive learning. We first
generate two different transformed versions of an image and feed them into two
sequential sub-networks, i.e., an encoder and a projection head. The
unsupervised training is achieved by maximizing the correspondence degree of
the outputs of the projection head. To evaluate the detection performance of
our unsupervised method, we further use the unsupervised features to train an
efficient linear classification network. Extensive experiments show that our
unsupervised learning method enables comparable detection performance to
state-of-the-art supervised techniques, in both the intra- and inter-dataset
settings. We also conduct ablation studies for our method.
