The face forgery detection problem has attracted wide attention in recent years. Although the vanilla convolutional neural network achieves promising results under the intra-domain testing scenario, it always fails to generalize to unseen scenarios. To address this problem, we propose Generalized Feature Space Learning (GFSL) with unknown forgery awareness, which leverages domain generalization to utilize face images forged with various methods. Considering that the true distribution of fake samples is harder to predict than the real samples, we regularize the model with an asymmetric triplet loss, aggregating only the real samples to learn an accurate real-image distribution, which forms an classification boundary that surrounds the real samples and generalizes well to unknown fake samples. Moreover, we apply Representation Self-Challenging (RSC) to perform selective dropout on features, which forces the model to learn more completed features rather than one or a few of the most prominent features, leading to better generalization ability. Extensive experiments show that our method consistently outperforms baseline models under various cross-manipulation-method tests and achieves comparable performance to the state-of-the-art methods on both intra- and cross-dataset evaluations.