期刊
WWW'20: COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2020
卷 -, 期 -, 页码 88-89出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3366424.3382711
关键词
Deepfake videos; Tampering detection; Multi-task learning
In this paper, we propose a novel Patch&Pair Convolutional Neural Networks (PPCNN) to distinguish Deepfake videos or images from real ones. Through the comprehensive evaluations on public datasets, we demonstrate that our model performs better than existing detection methods and show better generalization.
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