Abstract
Existing deepfake detection methods perform poorly on face forgeries generated by unseen face manipulation algorithms. The generalization ability of previous methods is mainly improved by modeling hand-crafted artifact features. Such properties, on the other hand, impede their further improvement. In this paper, we propose a novel deepfake detection method named Common Artifact Deepfake Detection Model, which aims to learn common artifact features in different face manipulation algorithms. To this end, we find that the main obstacle to learning common artifact features is that models are easily misled by the identity representation feature. We call this phenomenon Implicit Identity Leakage (IIL). Extensive experimental results demonstrate that, by learning the binary classifiers with the guidance of the Artifact Detection Module, our method effectively reduces the influence of IIL and outperforms the state-of-the-art by a large margin, proving that hand-crafted artifact feature detectors are not indispensable when tackling deepfake problems.
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URL
https://arxiv.org/abs/2210.14457
