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.

研究了 deepfake 检测器的泛化能力，发现现有模型难以适应未经训练的 deepfakes 数据集，但鉴于它们普遍学习合成方法的特定属性并难以提取区分特征，发现有神经元对已见和未见数据集都起到检测作用，为实现零样本泛化能力指明了可能的方向。