In this paper, we analyse the generalization ability of binary classifiers
for the task of deepfake detection. We find that the stumbling block to their
generalization is caused by the unexpected learned identity representation on
images. Termed as the Implicit Identity Leakage, this phenomenon has been
qualitatively and quantitatively verified among various DNNs. Furthermore,
based on such understanding, we propose a simple yet effective method named the
ID-unaware Deepfake Detection Model to reduce the influence of this phenomenon.
Extensive experimental results demonstrate that our method outperforms the
state-of-the-art in both in-dataset and cross-dataset evaluation. The code is
available at this https URL

本文研究在深度伪造检测任务中二元分类器的泛化能力，发现其泛化能力的障碍是由于图像中非预期的身份表示引起的隐式身份泄漏现象，并提出了 ID-unaware Deepfake Detection Model 方法来减少此现象的影响，实验结果表明我们的方法在数据集内和跨数据集评估中均优于现有技术。