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Diffusion based text-to-image models are trained on large datasets scraped from the Internet, potentially containing unacceptable concepts (e.g., copyright-infringing or unsafe).
Microsoft COCO: Common Objects in Context
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Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2023 · 2023
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Sanghyun Kim, Seohyeon Jung, Balhae Kim, Moonseok Choi, Jinwoo Shin, and Juho Lee. 2023 · 2023
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Getty Images suing the makers of popular AI art tool for allegedly stealing photos. In CNN Business . CNN
Jennifer Korn. 2023 · 2023
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Yiting Qu, Xinyue Shen, Xinlei He, Michael Backes, Savvas Zannettou, and Yang Zhang. 2023 · 2023
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Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE Computer Society
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting. 2023 · 2023
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Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE Computer Society, Los Alamitos, CA, USA, 6048–6058
G. Somepalli, V. Singla, M. Goldblum, J. Geiping, and T. Goldstein. 2023 · 2023
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Ring-A-Bell! How Reliable are Concept Removal Methods For Diffusion Models?. In The Twelfth International Conference on Learning Representations . OpenReview.net
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