  Deepfakes are becoming increasingly credible, posing a significant threat
given their potential to facilitate fraud or bypass access control systems.
This has motivated the development of deepfake detection methods, in which deep
learning models are trained to distinguish between real and synthesized
footage. Unfortunately, existing detectors struggle to generalize to deepfakes
from datasets they were not trained on, but little work has been done to
examine why or how this limitation can be addressed. Especially, those
single-modality deepfake images reveal little available forgery evidence,
posing greater challenges than detecting deepfake videos. In this work, we
present the first empirical study on the generalizability of deepfake
detectors, an essential goal for detectors to stay one step ahead of attackers.
Our study utilizes six deepfake datasets, five deepfake image detection
methods, and two model augmentation approaches, confirming that detectors do
not generalize in zero-shot settings. Additionally, we find that detectors are
learning unwanted properties specific to synthesis methods and struggling to
extract discriminative features, limiting their ability to generalize. Finally,
we find that there are neurons universally contributing to detection across
seen and unseen datasets, suggesting a possible path towards zero-shot
generalizability.
