This monograph explores zero-shot deepfake detection, an emerging method for when the models have never seen a particular deepfake variation. Topics discussed include self-supervised learning, transformer-based zero-shot classifier, generative model fingerprinting, and meta-learning techniques that better adapt to the ever-evolving deepfake threat.