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The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images.
A “soft” K-nearest neighbor voting scheme
Harvey B Mitchell and Paul A Schaefer · 2001
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
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Earlier work this paper cites.
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Duc-Tien Dang-Nguyen, Cecilia Pasquini, Valentina Conotter, and Giulia Boato · 2015
Earlier work this paper cites.
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Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, and Luisa Verdoliva · 2018
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
CNN-generated images are surprisingly easy to spot… for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros · 2020
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A Closer Look at Fourier Spectrum Discrepancies for CNN-Generated Images Detection
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