[38] C. Sahara, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479-36494, 2022.
[39] H. Salman, A. Ilyas, L. Engstrom, A. Kapoor, and A. Madry. Do adversarially robust imagenet models transfer better? Advances in Neural Information Processing Systems, 33:3533-3545, 2020.
[40] J. Song, C. Meng, and S. Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations, 2021.
[41] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.
[42] Y. Takagi and S. Nishimoto. High-resolution image reconstruction with latent diffusion models from human brain activity. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14453-14463, 2023.
[43] J. Wang, Z. Lyu, D. Lin, B. Dai, and H. Fu. Guided diffusion model for adversarial purification. arXiv preprint arXiv:2205.14969, 2022.
[44] Y. Wang, Z. Wu, C. Li, and A. Wu. Extraction and recovery of spatio-temporal structure in latent dynamics alignment with diffusion model. arXiv preprint arXiv:2306.06138, 2023.
[45] Q. Wu, H. Ye, and Y. Gu. Guided diffusion model for adversarial purification from random noise. arXiv preprint arXiv:2206.10875, 2022.
[46] C. Xiao, Z. Chen, K. Jin, J. Wang, W. Nie, M. Liu, A. Anandkumar, B. Li, and D. Song. Densepure: Understanding diffusion models for adversarial robustness. In The Eleventh International Conference on Learning Representations, 2023.
[47] K. Xu, G. Zhang, S. Liu, Q. Fan, M. Sun, H. Chen, P.-Y. Chen, Y. Wang, and X. Lin. Adversarial t-shirt! evading person detectors in a physical world. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part V 16, pages 665-681. Springer, 2020.
[48] X. Xu, X. Chen, C. Liu, A. Rohrbach, T. Darell, and D. Song. Can you fool ai with adversarial examples on a visual turing test. arXiv preprint arXiv:1709.08693, 3, 2017.
[49] H. Xue, C. Liang, X. Wu, and Y. Chen. Toward effective protection against diffusion based mimicry through score distillation. arXiv preprint arXiv:2311.12832, 2023.
[50] J. Yoon, S. J. Hwang, and J. Lee. Adversarial purification with score-based generative models. In International Conference on Machine Learning, pages 12062-12072. PMLR, 2021.
[51] S. Zagoruyko and N. Komodakis. Wide residual networks. In British Machine Vision Conference 2016. British Machine Vision Association, 2016.
[52] X. Zeng, C. Liu, Y.-S. Wang, W. Qiu, L. Xie, Y.-W. Tai, C.-K. Tang, and A. L. Yuille. Adversarial attacks beyond the image space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4302-4311, 2019.
[53] K. Zhang, H. Zhou, J. Zhang, Q. Huang, W. Zhang, and N. Yu. Ada3diff: Defending against 3d adversarial point clouds via adaptive diffusion. arXiv preprint arXiv:2211.16247, 2022.
[54] Q. Zhang and Y. Chen. Fast sampling of diffusion models with exponential integrator. arXiv preprint arXiv:2204.13902, 2022.
[55] Z. Zhao, D. Dua, and S. Singh. Generating natural adversarial examples. In International Conference on Learning Representations, 2018.