  As the saying goes, "seeing is believing". However, with the development of
digital face editing tools, we can no longer trust what we can see. Although
face forgery detection has made promising progress, most current methods are
designed manually by human experts, which is labor-consuming. In this paper, we
develop an end-to-end framework based on neural architecture search (NAS) for
deepfake detection, which can automatically design network architectures
without human intervention. First, a forgery-oriented search space is created
to choose appropriate operations for this task. Second, we propose a novel
performance estimation metric, which guides the search process to select more
general models. The cross-dataset search is also considered to develop more
general architectures. Eventually, we connect the cells in a cascaded pyramid
way for final forgery classification. Compared with state-of-the-art networks
artificially designed, our method achieves competitive performance in both
in-dataset and cross-dataset scenarios.
