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Given the rising popularity of AI-generated art and the associated copyright concerns, identifying whether an artwork was used to train a diffusion model is an important research topic.
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Logan: Membership inference attacks against generative models
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Understanding membership inferences on well-generalized learning models
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Monte carlo and reconstruction membership inference attacks against generative models
Hilprecht, B., Härterich, M., and Bernau, D · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Gan-leaks: A taxonomy of membership inference attacks against generative models
Chen, D., Yu, N., Zhang, Y., and Fritz, M · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A · 2020
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Quantifying privacy leakage in graph embedding
Duddu, V., Boutet, A., and Shejwalkar, V · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Hierarchical text-conditional image generation with clip latents
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Laion-5b: An open large-scale dataset for training next generation image-text models
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
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Information leakage in embedding models
Song, C. and Raghunathan, A · 2020
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
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Membership leakage in label-only exposures
Li, Z. and Zhang, Y · 2021
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Membership inference on word embedding and beyond
Mahloujifar, S., Inan, H. A., Chase, M., Ghosh, E., and Hasegawa, M · 2021
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Are diffusion models vulnerable to membership inference attacks?
Duan, J., Kong, F., Wang, S., Shi, X., and Xu, K · 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
Matsumoto, T., Miura, T., and Yanai, N · 2023
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Black-box membership inference attacks against fine-tuned diffusion models
Pang, Y. and Wang, T · 2023
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Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
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Membership inference attacks on diffusion models via quantile regression
Tang, S., Wu, Z. S., Aydore, S., Kearns, M., and Roth, A · 2023
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A latent space of stochastic diffusion models for zero-shot image editing and guidance
Wu, C. H. and De la Torre, F · 2023
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Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation
Wu, J. Z., Ge, Y., Wang, X., Lei, S. W., Gu, Y., Shi, Y., Hsu, W., Shan, Y., Qie, X., and Shou, M. Z · 2023
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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 · 2023
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Scaling rectified flow transformers for high-resolution image synthesis
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., et al · 2024
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