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We propose Deep Distribution Transfer(DDT), a new transfer learning approach to address the problem of zero and few-shot transfer in the context of facial forgery detection.
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Brian Dolhansky, Russ Howes, Ben Pflaum, Nicole Baram, and Cristian Canton Ferrer · 2019
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Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, and In So Kweon · 2019
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Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen · 2018
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Two-stream neural networks for tampered face detection, Jul 2017
Peng Zhou, Xintong Han, Vlad I. Morariu, and Larry S. Davis · 2019
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Deeperforensics-1.0: A large-scale dataset for real-world face forgery detection
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Celeb-df: A large-scale challenging dataset for deepfake forensics
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