Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall short of practical application. Additionally, finetuning these methods on historical training data is resource-intensive in terms of time and storage. In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones. Specifically, we propose a Historical Distribution Preserving (HDP) framework that reserves and preserves the distributions of historical faces. To achieve this, we use universal adversarial perturbation (UAP) to simulate historical forgery distribution, and knowledge distillation to maintain the distribution variation of real faces across different models. We also construct a new benchmark for CFFD with three evaluation protocols. Our extensive experiments on the benchmarks show that our method outperforms the state-of-the-art competitors. Our code is available at https://github.com/skJack/HDP.

人脸伪造技术发展迅速，并构成严重的安全威胁。现有的人脸伪造检测方法试图学习可泛化的特征，但仍然缺乏实际应用。此外，根据历史训练数据微调这些方法在时间和存储方面都是资源密集型的。在本文中，我们关注一个新颖且具有挑战性的问题：持续人脸伪造检测（CFFD），其目的是有效地从新的伪造攻击中学习，而不忘记以前的攻击。具体来说，我们提出了一个历史分布保留（HDP）框架，用于保留和保留历史面孔的分布。为了实现这一目标，我们使用通用对抗扰动（UAP）来模拟历史伪造分布，并使用知识蒸馏来维持不同模型中真实面孔的分布变化。我们还通过三种评估协议构建了 CFFD 的新基准。我们对基准进行的广泛实验表明，我们的方法优于最先进的竞争对手。我们的代码可在 https://github.com/skJack/HDP 获取。