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Diffusion models have begun to overshadow GANs and other generative models in industrial applications due to their superior image generation performance.
Membership inference attacks from first principles
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., and Tramer, F · 1914
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Label-only membership inference attacks
Choquette-Choo, C. A., Tramer, F., Carlini, N., and Papernot, N · 1974
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Differential privacy: A survey of results
Dwork, C · 2008
Earlier work this paper cites.
Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J. V., Stephan, D. A., Nelson, S. F., and Craig, D. W · 2008
Earlier work this paper cites.
Visualizing data using t-sne
van der Maaten, L., and Hinton, G. E · 2008
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
DeVries, T., and Taylor, G. W · 2017
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Logan: Membership inference attacks against generative models
Hayes, J., Melis, L., Danezis, G., and De Cristofaro, E · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Generative adversarial networks: An overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., and Bharath, A. A · 2018
Earlier work this paper cites.
Property inference attacks on fully connected neural networks using permutation invariant representations
Ganju, K., Wang, Q., Yang, W., Gunter, C. A., and Borisov, N · 2018
Earlier work this paper cites.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2018
Earlier work this paper cites.
Variational autoencoders for collaborative filtering
Liang, D., Krishnan, R. G., Hoffman, M. D., and Jebara, T · 2018
Earlier work this paper cites.
Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., and Backes, M · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Yeom, S., Giacomelli, I., Fredrikson, M., and Jha, S · 2018
Earlier work this paper cites.
Monte carlo and reconstruction membership inference attacks against generative models
Hilprecht, B., Härterich, M., and Bernau, D · 2019
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Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning
Nasr, M., Shokri, R., and Houmansadr, A · 2019
Earlier work this paper cites.
White-box vs black-box: Bayes optimal strategies for membership inference
Sablayrolles, A., Douze, M., Schmid, C., Ollivier, Y., and Jégou, H · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Song, Y., and Ermon, S · 2019
Earlier work this paper cites.
Gan-leaks: A taxonomy of membership inference attacks against generative models
Chen, D., Yu, N., Zhang, Y., and Fritz, M · 2020
Earlier work this paper cites.
Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Cited alongside, same era.
Towards the infeasibility of membership inference on deep models
Rezaei, S., and Liu, X · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
Cited alongside, same era.
Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
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Hierarchical text-conditional image generation with clip latents, 2022
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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High-resolution image synthesis with latent diffusion models, 2022
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Palette: Image-to-image diffusion models
Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., and Norouzi, M · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al · 2022
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Image super-resolution via iterative refinement
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Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P., and Nichol, A · 2021
Cited alongside, same era.
Cogview: Mastering text-to-image generation via transformers
Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., et al · 2021
Cited alongside, same era.
Membership inference attacks against gans by leveraging over-representation regions
Hu, H., and Pang, J · 2021
Cited alongside, same era.
Practical blind membership inference attack via differential comparisons
Hui, B., Yang, Y., Yuan, H., Burlina, P., Gong, N. Z., and Cao, Y · 2021
Cited alongside, same era.
Membership inference attacks and defenses in classification models
Li, J., Li, N., and Ribeiro, B · 2021
Cited alongside, same era.
Sdedit: Guided image synthesis and editing with stochastic differential equations
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 2021
Cited alongside, same era.
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., and Norouzi, M · 2022
Later among the works it cites.
Diffusers: State-of-the-art diffusion models
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., and Wolf, T · 2022
Later among the works it cites.
Membership inference attacks against text-to-image generation models
Wu, Y., Yu, N., Li, Z., Backes, M., and Zhang, Y · 2022
Later among the works it cites.
Enhanced membership inference attacks against machine learning models
Ye, J., Maddi, A., Murakonda, S. K., Bindschaedler, V., and Shokri, R · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., et al · 2022
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All are worth words: A vit backbone for diffusion models, 2023
Bao, F., Nie, S., Xue, K., Cao, Y., Li, C., Su, H., and Zhu, J · 2023
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Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., and Wallace, E · 2023
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Are diffusion models vulnerable to membership inference attacks?, 2023
Duan, J., Kong, F., Wang, S., Shi, X., and Xu, K · 2023
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Privacy distillation: Reducing re-identification risk of multimodal diffusion models, 2023
Fernandez, V., Sanchez, P., Pinaya, W. H. L., Jacenków, G., Tsaftaris, S. A., and Cardoso, J · 2023
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Learning controllable 3d diffusion models from single-view images
Gu, J., Gao, Q., Zhai, S., Chen, B., Liu, L., and Susskind, J · 2023
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Membership inference of diffusion models
Hu, H., and Pang, J · 2023
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An efficient membership inference attack for the diffusion model by proximal initialization
Kong, F., Duan, J., Ma, R., Shen, H., Zhu, X., Shi, X., and Xu, K · 2023
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Membership inference attacks against diffusion models, 2023
Matsumoto, T., Miura, T., and Yanai, N · 2023
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Protecting the intellectual property of diffusion models by the watermark diffusion process, 2023
Peng, S., Chen, Y., Wang, C., and Jia, X · 2023
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Glaze: Protecting artists from style mimicry by text-to-image models, 2023
Shan, S., Cryan, J., Wenger, E., Zheng, H., Hanocka, R., and Zhao, B. Y · 2023
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Demystifying membership inference attacks in machine learning as a service
Truex, S., Liu, L., Gursoy, M. E., Yu, L., and Wei, W · 2089
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Membership inference attacks by exploiting loss trajectory
Liu, Y., Zhao, Z., Backes, M., and Zhang, Y · 2098
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