  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.
