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The widespread adoption of generative image models has highlighted the urgent need to detect artificial content, which is a crucial step in combating widespread manipulation and misinformation.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Detecting and simulating artifacts in gan fake images
Xu Zhang, Svebor Karaman, and Shih-Fu Chang · 2019
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Large scale gan training for high fidelity natural image synthesis. international conference on learning representations
A Brock, J Donahue, and K Simonyan · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions
Ricard Durall, Margret Keuper, and Janis Keuper · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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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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Are gan generated images easy to detect? a critical analysis of the state-of-the-art
Diego Gragnaniello, Davide Cozzolino, Francesco Marra, Giovanni Poggi, and Luisa Verdoliva · 2021
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Towards universal gan image detection
Davide Cozzolino, Diego Gragnaniello, Giovanni Poggi, and Luisa Verdoliva · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
Cited alongside, same era.
Towards the detection of diffusion model deepfakes
Jonas Ricker, Simon Damm, Thorsten Holz, and Asja Fischer · 2022
Cited alongside, same era.
Towards universal fake image detectors that generalize across generative models
Utkarsh Ojha, Yuheng Li, and Yong Jae Lee · 2023
Later among the works it cites.
Gendet: Towards good generalizations for ai-generated image detection
Mingjian Zhu, Hanting Chen, Mouxiao Huang, Wei Li, Hailin Hu, Jie Hu, and Yunhe Wang · 2023
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Dire for diffusion-generated image detection
Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, and Houqiang Li · 2023
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Shadows don’t lie and lines can’t bend! generative models don’t know projective geometry… for now
Ayush Sarkar, Hanlin Mai, Amitabh Mahapatra, Svetlana Lazebnik, David A Forsyth, and Anand Bhattad · 2023
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Online detection of ai-generated images
David C. Epstein, Ishan Jain, Oliver Wang, and Richard Zhang · 2023
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Compression in the ImageNet dataset
towardsdatascience
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Wouaf: Weight modulation for user attribution and fingerprinting in text-to-image diffusion models
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Seeing is not always believing: Benchmarking human and model perception of ai-generated images
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Genimage: A million-scale benchmark for detecting ai-generated image
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