References

  1. Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramèr, F., Balle, B., Ippolito, D., & Wallace, E. (2023). Extracting training data from diffusion models. In 32nd USENIX Security Symposium (USENIX Security 23) (pp. 5253–5270). USENIX Association. https://www.usenix.org/conference/usenixsecurity23/presentation/carlini
  2. Dubiński, J., Kowalczuk, A., Boenisch, F., & Dziedzic, A. (2025). CDI: Copyrighted data identification in diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 18674–18684). IEEE. https://doi.org/10.1109/CVPR52734.2025.01740
  3. Lovato, J., Zimmerman, J. W., Smith, I., Dodds, P., & Karson, J. L. (2024). Foregrounding artist opinions: A survey study on transparency, ownership, and fairness in AI generative art. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7(1), 905–916. https://doi.org/10.1609/aies.v7i1.31691
  4. Schor, Z. (2024, December 2). Andersen v. Stability AI: The landmark case unpacking the copyright risks of AI image generators. NYU Journal of Intellectual Property & Entertainment Law. https://jipel.law.nyu.edu/andersen-v-stability-ai-the-landmark-case-unpacking-thecopyri ght-risks-of-ai-image-generators/
  5. McIntyre, J. H. (2026). The right to restrict AI training. Philosophy & Technology, 39(1), 25. .https://doi.org/10.1007/s13347-025-01032-x
  6. Shan, S., Cryan, J., Wenger, E., Zheng, H., Hanocka, R., & Zhao, B. Y. (2023). Glaze: Protecting artists from style mimicry by text-to-image models. In 32nd USENIX Security Symposium (USENIX Security 23) (pp. 2187–2204). USENIX Association. https://www.usenix.org/conference/usenixsecurity23/presentation/shan