Towards A Robust Deepfake Detector:Common Artifact Deepfake Detection
Model
- URL: http://arxiv.org/abs/2210.14457v1
- Date: Wed, 26 Oct 2022 04:02:29 GMT
- Title: Towards A Robust Deepfake Detector:Common Artifact Deepfake Detection
Model
- Authors: Shichao Dong, Jin Wang, Renhe Ji, Jiajun Liang, Haoqiang Fan and Zheng
Ge
- Abstract summary: We propose a novel deepfake detection method named Common Artifact Deepfake Detection Model.
We find that the main obstacle to learning common artifact features is that models are easily misled by the identity representation feature.
Our method effectively reduces the influence of Implicit Identity Leakage and outperforms the state-of-the-art by a large margin.
- Score: 14.308886041268973
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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