Mist: Towards Improved Adversarial Examples for Diffusion Models

Deep unsupervised learning using nonequilibrium thermodynamics

Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015

Earlier work this paper cites.

Painter by numbers, wikiart

Nichol., K · 2016

Earlier work this paper cites.

Towards Deep Learning Models Resistant to Adversarial Attacks

Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018

Earlier work this paper cites.

Denoising diffusion probabilistic models

Ho, J., Jain, A., and Abbeel, P · 2020

Earlier work this paper cites.

Score-based generative modeling through stochastic differential equations

Original

Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020

Earlier work this paper cites.

Diffusion models beat gans on image synthesis

Dhariwal, P. and Nichol, A · 2021

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An image is worth one word: Personalizing text-to-image generation using textual inversion

Original

Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., and Cohen-Or, D · 2022

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High-resolution image synthesis with latent diffusion models

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022

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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Original

Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., and Aberman, K · 2022

Cited alongside, same era.

Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples, 2023

Liang, C., Wu, X., Hua, Y., Zhang, J., Xue, Y., Song, T., Xue, Z., Ma, R., and Guan, H · 2023

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Raising the cost of malicious ai-powered image editing

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Salman, H., Khaddaj, A., Leclerc, G., Ilyas, A., and Madry, A · 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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Anti-dreambooth: Protecting users from personalized text-to-image synthesis

Original

Van Le, T., Phung, H., Nguyen, T. H., Dao, Q., Tran, N., and Tran, A · 2023

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