Fetching the paper…
Reading the bibliography…
Advancements in open-sourced text-to-image models and fine-tuning methods have led to the increasing risk of malicious adaptation, i.e., fine-tuning to generate harmful/unauthorized content.
Larsen, J., Hansen, L.K., Svarer, C., Ohlsson, M.: Design and regularization of neural networks: the optimal use of a validation set. In: IEEE Signal Processing Society Workshop (1996)
1996
Earlier work this paper cites.
Bengio, Y.: Gradient-based optimization of hyperparameters. Neural Computation (2000)
2000
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: Proc. CVPR (2009)
2009
Earlier work this paper cites.
Biggio, B., Nelson, B., Laskov, P.: Support vector machines under adversarial label noise. In: Proc. ACML (2011)
2011
Earlier work this paper cites.
Vincent, P.: A connection between score matching and denoising autoencoders. Neural computation (2011)
2011
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Proc. ICLR (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Proc. ICLR (2015)
2015
Earlier work this paper cites.
Mei, S., Zhu, X.: Using machine teaching to identify optimal training-set attacks on machine learners. In: Proc. AAAI (2015)
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: Proc. ICML (2015)
2015
Earlier work this paper cites.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proc. ICML (2017)
2017
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proc. CVPR (2018)
2018
Earlier work this paper cites.
Bedapudi, P.: NudeNet: Neural nets for nudity classification, detection and selective censoring. https://github.com/platelminto/NudeNetClassifier (2019)
2019
Earlier work this paper cites.
Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. In: Proc. NeurIPS (2019)
2019
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Proc. NeurIPS (2020)
2020
Earlier work this paper cites.
Lorraine, J., Vicol, P., Duvenaud, D.: Optimizing millions of hyperparameters by implicit differentiation. In: Proc. AISTATS (2020)
2020
Earlier work this paper cites.
Ren, Z., Yeh, R., Schwing, A.: Not all unlabeled data are equal: Learning to weight data in semi-supervised learning (2020)
2020
Earlier work this paper cites.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proc. CVPR (2021)
2021
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Proc. NeurIPS (2021)
2021
Cited alongside, same era.
Kingma, D., Salimans, T., Poole, B., Ho, J.: Variational diffusion models. In: Proc. NeurIPS (2021)
2021
Cited alongside, same era.
Heikkilä, M.: This artist is dominating ai-generated art. and he’s not happy about it. MIT Technology Review. Retrieved March 16
2022
Cited alongside, same era.
Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: Proc. ICLR (2022)
2022
Cited alongside, same era.
Nichol, A.Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In: Proc. ICML (2022)
2022
Gandikota, R., Materzyńska, J., Fiotto-Kaufman, J., Bau, D.: Erasing concepts from diffusion models. In: Proc. ICCV (2023)
2023
Closest in time.
Harwell, D.: AI-generated child sex images spawn new nightmare for the web. The Washington Post (2023), URL https://www.washingtonpost.com/technology/2023/06/19/artificial-intelligence-child-sex-abuse-images/
2023
Closest in time.
2023
Closest in time.
Liang, C., Wu, X.: Mist: Towards improved adversarial examples for diffusion models (2023)
2023
Closest in time.
Liang, C., Wu, X., Hua, Y., Zhang, J., Xue, Y., Song, T., Xue, Z., Ma, R., Guan, H.: Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples. In: Proc. ICML (2023)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
2022
Cited alongside, same era.
Rando, J., Paleka, D., Lindner, D., Heim, L., Tramèr, F.: Red-teaming the stable diffusion safety filter. In: Proc. NeurIPS ML Safety Workshop (2022)
2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proc. CVPR (2022)
2022
Cited alongside, same era.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., Ho, J., Fleet, D.J., Norouzi, M.: Photorealistic text-to-image diffusion models with deep language understanding. In: Proc. NeurIPS (2022)
2022
Cited alongside, same era.
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C.W., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S.R., Crowson, K., Schmidt, L., Kaczmarczyk, R., Jitsev, J.: LAION-5B: An open large-scale dataset for training next generation image-text models. In: Proc. NeurIPS (2022)
2022
Cited alongside, same era.
SmithMano: Tutorial: How to remove the safety filter in 5 seconds. Reddit (2022)
2022
Cited alongside, same era.
Yeh, R.A., Hu, Y.T., Hasegawa-Johnson, M., Schwing, A.: Equivariance discovery by learned parameter-sharing. In: Proc. AISTATS (2022)
2022
Cited alongside, same era.
2023
Closest in time.
Moore, S.: Can the law prevent AI from duplicating actors? It’s complicated. Forbes (Jul 2023), URL https://www.forbes.com/sites/schuylermoore/2023/07/13/protecting-celebrities-including-all-actors-from-ai-with-the-right-of-publicity/?sh=5c56ba4159ec
2023
Closest in time.
Noveck, J., O’Brien, M.: Visual artists sue ai companies in sf federal court for repurposing their work. Associated Press (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: Proc. CVPR (2023)
2023
Closest in time.
Salman, H., Khaddaj, A., Leclerc, G., Ilyas, A., Madry, A.: Raising the cost of malicious AI-powered image editing. In: Proc. ICML (2023)
2023
Closest in time.
Schramowski, P., Brack, M., Deiseroth, B., Kersting, K.: Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models. In: Proc. CVPR (2023)
2023
Closest in time.
Shan, S., Cryan, J., Wenger, E., Zheng, H., Hanocka, R., Zhao, B.Y.: Glaze: Protecting artists from style mimicry by text-to-image models. In: USENIX Security Symposium (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Gandikota, R., Orgad, H., Belinkov, Y., Materzyńska, J., Bau, D.: Unified concept editing in diffusion models. In: Proc. WACV (2024)
2024
Closest in time.