Although supervised face deepfake detection techniques yield impressive results, they require labels, which may not be often available. In contrast, Fung et al. have designed an unsupervised approach to deepfake detection that matches the performance of supervised systems by using contrastive learning. Their model first generates two transformed versions of the face, which are fed to two sequential subnetworks to maximize their agreement.