Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
This addresses the problem of poor generalization in deepfake detection for security and media applications, offering a novel solution to a known bottleneck.
The paper identifies Implicit Identity Leakage as a key issue limiting the generalization of deepfake detection classifiers, and proposes an ID-unaware model that outperforms state-of-the-art methods in both in-dataset and cross-dataset evaluations.
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 https://github.com/megvii-research/CADDM.