Towards generalizable detection of face forgery via self-guided model-agnostic learning

Face forgery detection is an important yet challenging task that aims to distinguish whether a face video has been modified. As various types of face forgery are constantly produced and made available, existing methods usually overfit to the manipulation methods they are trained for, and cannot generalize well to detect the unseen or unknown forgery types. To address this issue, we present a systematic study on a more generalizable solution of face forgery detection, which endows the model an ability to recognize fake videos with unpredictable forgery types. Specifically, we develop a model-agnostic learning approach with a gradient-based meta-train and meta-test procedure to simulate the domain shift from known to unknown forgery types. To further emphasize the relative importance of different available forgery types during training, we propose a self-guided importance sampling strategy, which is integrated with a general video-level classification network. We also build a dataset with a wide range of 10 different forgery types to benchmark the of face forgery detection. Extensive experiments on multiple testing protocols of evaluating generalization ability show that our method generalizes significantly better on unknown forgery manipulations.