Existing face forgery detection models try to discriminate fake images by detecting only spatial artifacts (e.g., generative artifacts, blending) or mainly temporal artifacts (e.g., flickering, discontinuity). They may experience significant performance degradation when facing out-domain artifacts. In this paper, we propose to capture both spatial and temporal artifacts in one model for face forgery detection. A simple idea is to leverage a spatiotemporal model (3D ConvNet). However, we find that it may easily rely on one type of artifact and ignore the other. To address this issue, we present a novel training strategy called AltFreezing for more general face forgery detection. The AltFreezing aims to encourage the model to detect both spatial and temporal artifacts. It divides the weights of a spatiotemporal network into two groups: spatial-related and temporal-related. Then the two groups of weights are alternately frozen during the training process so that the model can learn spatial and temporal features to distinguish real or fake videos. Furthermore, we introduce various video-level data augmentation methods to improve the generalization capability of the forgery detection model. Extensive experiments show that our framework outperforms existing methods in terms of generalization to unseen manipulations and datasets. Code is available at https: //github.com/ZhendongWang6/AltFreezing.

翻译：现有的人脸伪造检测模型通常仅通过检测空间伪影（如生成痕迹、融合边界）或时间伪影（如闪烁、不连续性）来判别伪造图像。当面临域外伪影时，此类模型性能会显著下降。本文提出在同一模型中同时捕获空间与时间伪影以实现人脸伪造检测。直观思路是采用时空模型（3D卷积网络），但研究发现这类模型易偏向单一伪影类型而忽略另一种。为解决该问题，我们提出一种名为"交替冻结法"的新型训练策略，用于实现更通用的人脸伪造检测。该方法旨在促使模型同时检测空间与时间伪影：将时空网络权重分为空间相关和时间相关两组，在训练过程中交替冻结这两组权重，使模型能够同时学习区分真实/虚假视频所需的空间特征与时间特征。此外，我们引入多种视频级数据增强方法以提升伪造检测模型的泛化能力。大量实验表明，本框架在面对未见过的操作类型与数据集时，其泛化性能优于现有方法。代码已开源：https://github.com/ZhendongWang6/AltFreezing。