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Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffusion models.
Learning multiple layers of features from tiny images, 2009
A. Krizhevsky, G. Hinton, et al · 2009
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Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon · 2010
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Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2011
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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Knock knock, who’s there? membership inference on aggregate location data
A. Pyrgelis, C. Troncoso, and E. De Cristofaro · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
M. Nasr, R. Shokri, and A. Houmansadr · 2019
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White-box vs black-box: Bayes optimal strategies for membership inference
A. Sablayrolles, M. Douze, C. Schmid, Y. Ollivier, and H. Jégou · 2019
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Generative modeling by estimating gradients of the data distribution
Y. Song and S. Ermon · 2019
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Demystifying membership inference attacks in machine learning as a service
S. Truex, L. Liu, M. E. Gursoy, L. Yu, and W. Wei · 2019
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Generative adversarial networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2020
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro · 2020
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Embedded encoder-decoder in convolutional networks towards explainable ai
A. Tavanaei · 2020
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Attribute-aware generative design with generative adversarial networks
C. Yuan and M. Moghaddam · 2020
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Label-only membership inference attacks
C. A. Choquette-Choo, F. Tramer, N. Carlini, and N. Papernot · 2021
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Diffusion models beat gans on image synthesis
Pseudo numerical methods for diffusion models on manifolds
L. Liu, Y. Ren, Z. Lin, and Z. Zhao · 2022
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Hierarchical text-conditional image generation with clip latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
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Towards the detection of diffusion model deepfakes
J. Ricker, S. Damm, T. Holz, and A. Fischer · 2022
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High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al · 2022
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P. Dhariwal and A. Nichol · 2021
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Score-based generative modeling with critically-damped langevin diffusion
T. Dockhorn, A. Vahdat, and K. Kreis · 2021
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Practical blind membership inference attack via differential comparisons
B. Hui, Y. Yang, H. Yuan, P. Burlina, N. Z. Gong, and Y. Cao · 2021
Cited alongside, same era.
Sdedit: Guided image synthesis and editing with stochastic differential equations
C. Meng, Y. He, Y. Song, J. Song, J. Wu, J.-Y. Zhu, and S. Ermon · 2021
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On the difficulty of membership inference attacks
S. Rezaei and X. Liu · 2021
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Systematic evaluation of privacy risks of machine learning models
L. Song and P. Mittal · 2021
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Tackling the generative learning trilemma with denoising diffusion gans
Z. Xiao, K. Kreis, and A. Vahdat · 2021
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Progressive distillation for fast sampling of diffusion models
T. Salimans and J. Ho · 2022
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Learning fast samplers for diffusion models by differentiating through sample quality
D. Watson, W. Chan, J. Ho, and M. Norouzi · 2022
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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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Are diffusion models vulnerable to membership inference attacks?
J. Duan, F. Kong, S. Wang, X. Shi, and K. Xu · 2023
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An efficient membership inference attack for the diffusion model by proximal initialization
F. Kong, J. Duan, R. Ma, H. Shen, X. Zhu, X. Shi, and K. Xu · 2023
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Membership inference attacks against diffusion models
T. Matsumoto, T. Miura, and N. Yanai · 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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Dde-gan: Integrating a data-driven design evaluator into generative adversarial networks for desirable and diverse concept generation
C. Yuan, T. Marion, and M. Moghaddam · 2023
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Unlearnable examples for diffusion models: Protect data from unauthorized exploitation
Z. Zhao, J. Duan, X. Hu, K. Xu, C. Wang, R. Zhang, Z. Du, Q. Guo, and Y. Chen · 2023
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