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We study universal deepfake detection.
“What makes fake images detectable? understanding properties that generalize,”
L. Chai, D. Bau, S. Lim, and P. Isola, · 2020
Earlier work this paper cites.
“The creation and detection of deepfakes,”
Yisroel Mirsky and Wenke Lee, · 2020
Earlier work this paper cites.
“Cnn-generated images are surprisingly easy to spot… for now,”
S. Wang, O. Wang, R. Zhang, A. Owens, and A. A. Efros, · 2020
Earlier work this paper cites.
“Are GAN generated images easy to detect? A critical analysis of the state-of-the-art,”
D. Gragnaniello, D. Cozzolino, F. Marra, G. Poggi, and L. Verdoliva, · 2021
Earlier work this paper cites.
“High-resolution image synthesis with latent diffusion models,”
Robin Rombach, A. Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer, · 2021
Earlier work this paper cites.
“Learning transferable visual models from natural language supervision,”
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever, · 2021
Earlier work this paper cites.
“Discovering transferable forensic features for cnn-generated images detection,”
K. Chandrasegaran, N. Tran, A. Binder, and N. Cheung, · 2022
Cited alongside, same era.
“OST: improving generalization of deepfake detection via one-shot test-time training,”
L. Chen, Y. Zhang, Y. Song, J. Wang, and L. Liu, · 2022
Cited alongside, same era.
“Masked autoencoders are scalable vision learners,”
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. B. Girshick, · 2022
Cited alongside, same era.
“Masked generative adversarial networks are data-efficient generation learners,”
J. Huang, K. Cui, D. Guan, A. Xiao, F. Zhan, S. Lu, S. Liao, and E. Xing, · 2022
Cited alongside, same era.
“Towards universal fake image detectors that generalize across generative models,”
U. Ojha, Y. Li, and Y. Lee, · 2023
Cited alongside, same era.
“A survey on generative modeling with limited data, few shots, and zero shot,”
Milad Abdollahzadeh, Touba Malekzadeh, Christopher T. H. Teo, Keshigeyan Chandrasegaran, Guimeng Liu, and Ngai-Man Cheung, · 2023
Later among the works it cites.
“Rethinking out-of-distribution (ood) detection: Masked image modeling is all you need,”
J. Li, P. Chen, S. Yu, Z. He, S. Liu, and J. Jia, · 2023
Later among the works it cites.
“Masked frequency modeling for self-supervised visual pre-training,”
J. Xie, W. Li, X. Zhan, Z. Liu, Y. Ong, and C. Loy, · 2023
Later among the works it cites.
“Intriguing properties of synthetic images: from generative adversarial networks to diffusion models,”
R. Corvi, D. Cozzolino, G. Poggi, K. Nagano, and L. Verdoliva, · 2023
Later among the works it cites.
“On the detection of synthetic images generated by diffusion models,”
R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, and L. Verdoliva, · 2023
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