How Generalizable are Deepfake Detectors? An Empirical Study
Boquan Li Jun Sun Christopher M. Poskitt
Deepfake videos and images 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 detection models 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. In this
paper, 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 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, illuminating a possible path forward to zero-shot
generalizability.