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Generative models now produce images with such stunning realism that they can easily deceive the human eye.
Visualizing data using t-sne
L. Van der Maaten and G. Hinton · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2015
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
T. Karras · 2017
Earlier work this paper cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Earlier work this paper cites.
Large scale gan training for high fidelity natural image synthesis
A. Brock · 2018
Earlier work this paper cites.
Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo · 2018
Earlier work this paper cites.
Large-scale celebfaces attributes (celeba) dataset
Z. Liu, P. Luo, X. Wang, and X. Tang · 2018
Earlier work this paper cites.
‘fake news’ is the invention of a liar: How false information circulates within the hybrid news system
F. Giglietto, L. Iannelli, A. Valeriani, and L. Rossi · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
Earlier work this paper cites.
Semantic image synthesis with spatially-adaptive normalization
T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu · 2019
Earlier work this paper cites.
Exploiting multi-domain visual information for fake news detection
P. Qi, J. Cao, T. Yang, J. Guo, and J. Li · 2019
Earlier work this paper cites.
The emergence of deepfake technology: A review
M. Westerlund · 2019
Earlier work this paper cites.
What makes fake images detectable? understanding properties that generalize
L. Chai, D. Bau, S.-N. Lim, and P. Isola · 2020
Earlier work this paper cites.
Leveraging frequency analysis for deep fake image recognition
J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, and T. Holz · 2020
Earlier work this paper cites.
Generative adversarial networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Analyzing and improving the image quality of stylegan
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2020
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Thinking in frequency: Face forgery detection by mining frequency-aware clues
Y. Qian, G. Yin, L. Sheng, Z. Chen, and J. Shao · 2020
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Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon · 2020
Cited alongside, same era.
Cnn-generated images are surprisingly easy to spot… for now
S.-Y. Wang, O. Wang, R. Zhang, A. Owens, and A. A. Efros · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
Detecting deepfakes with self-blended images
K. Shiohara and T. Yamasaki · 2022
Later among the works it cites.
Improving image generation with better captions
J. Betker, G. Goh, L. Jing, T. Brooks, J. Wang, L. Li, L. Ouyang, J. Zhuang, J. Lee, Y. Guo, et al · 2023
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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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Q-diffusion: Quantizing diffusion models
X. Li, Y. Liu, L. Lian, H. Yang, Z. Dong, D. Kang, S. Zhang, and K. Keutzer · 2023
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Scalable diffusion models with transformers
W. Peebles and S. Xie · 2023
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Wavelet diffusion models are fast and scalable image generators
H. Phung, Q. Dao, and A. Tran · 2023
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Alias-free generative adversarial networks
T. Karras, M. Aittala, S. Laine, E. Härkönen, J. Hellsten, J. Lehtinen, and T. Aila · 2021
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Privacy and artificial intelligence: challenges for protecting health information in a new era
B. Murdoch · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen · 2021
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Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
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End-to-end reconstruction-classification learning for face forgery detection
J. Cao, C. Ma, T. Yao, S. Chen, S. Ding, and X. Yang · 2022
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Vector quantized diffusion model for text-to-image synthesis
S. Gu, D. Chen, J. Bao, F. Wen, B. Zhang, D. Chen, L. Yuan, and B. Guo · 2022
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Z. Shi, H. Chen, L. Chen, and D. Zhang · 2023
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Deep image fingerprint: Accurate and low budget synthetic image detector
S. Sinitsa and O. Fried · 2023
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Learning on gradients: Generalized artifacts representation for gan-generated images detection
C. Tan, Y. Zhao, S. Wei, G. Gu, and Y. Wei · 2023
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The advanced proprietary ai/ml solution as anti-fraudtensorlink4cheque (aftl4c) for cheque fraud detection
P. Uyyala and D. C. Yadav · 2023
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Dire for diffusion-generated image detection
Z. Wang, J. Bao, W. Zhou, W. Wang, H. Hu, H. Chen, and H. Li · 2023
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Rich and poor texture contrast: A simple yet effective approach for ai-generated image detection
N. Zhong, Y. Xu, Z. Qian, and X. Zhang · 2023
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Drct: Diffusion reconstruction contrastive training towards universal detection of diffusion generated images
B. Chen, J. Zeng, J. Yang, and R. Yang · 2024
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Fake or jpeg? revealing common biases in generated image detection datasets
P. Grommelt, L. Weiss, F.-J. Pfreundt, and J. Keuper · 2024
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Forgery-aware adaptive transformer for generalizable synthetic image detection
H. Liu, Z. Tan, C. Tan, Y. Wei, J. Wang, and Y. Zhao · 2024
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Lareˆ 2: Latent reconstruction error based method for diffusion-generated image detection
Y. Luo, J. Du, K. Yan, and S. Ding · 2024
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Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection
C. Tan, Y. Zhao, S. Wei, G. Gu, P. Liu, and Y. Wei · 2024
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Genimage: A million-scale benchmark for detecting ai-generated image
M. Zhu, H. Chen, Q. Yan, X. Huang, G. Lin, W. Li, Z. Tu, H. Hu, J. Hu, and Y. Wang · 2024
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