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.

翻译：常言道“眼见为实”，然而随着数字人脸编辑工具的发展，我们已无法再相信所见之物。尽管人脸伪造检测已取得显著进展，但现有方法大多由人类专家手动设计，耗费大量人力。本文基于神经架构搜索（NAS）开发了一种端到端的深度伪造检测框架，可在无需人工干预的情况下自动设计网络架构。首先，构建了面向伪造操作的搜索空间，为该任务选择合适算子；其次，提出一种新颖的性能评估指标，引导搜索过程选择更具泛化性的模型；同时引入跨数据集搜索以开发更通用的架构。最终，通过级联金字塔方式连接各细胞单元完成伪造分类。与人工设计的当前最优网络相比，本方法在数据集内及跨数据集场景下均实现了具有竞争力的性能。