  Recent studies in deepfake detection have yielded promising results when the
training and testing face forgeries are from the same dataset. However, the
problem remains challenging when one tries to generalize the detector to
forgeries created by unseen methods in the training dataset. This work
addresses the generalizable deepfake detection from a simple principle: a
generalizable representation should be sensitive to diverse types of forgeries.
Following this principle, we propose to enrich the "diversity" of forgeries by
synthesizing augmented forgeries with a pool of forgery configurations and
strengthen the "sensitivity" to the forgeries by enforcing the model to predict
the forgery configurations. To effectively explore the large forgery
augmentation space, we further propose to use the adversarial training strategy
to dynamically synthesize the most challenging forgeries to the current model.
Through extensive experiments, we show that the proposed strategies are
surprisingly effective (see Figure 1), and they could achieve superior
performance than the current state-of-the-art methods. Code is available at
\url{https://github.com/liangchen527/SLADD}.
