| Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples C Liang, X Wu, Y Hua, J Zhang, Y Xue, T Song, Z Xue, R Ma, H Guan arXiv preprint arXiv:2302.04578, 2023 | 43 | 2023 |
| Mist: Towards improved adversarial examples for diffusion models C Liang, X Wu arXiv preprint arXiv:2305.12683, 2023 | 15 | 2023 |
| Understanding and improving adversarial attacks on latent diffusion model B Zheng, C Liang, X Wu, Y Liu arXiv preprint arXiv:2310.04687, 2023 | 10 | 2023 |
| Toward effective protection against diffusion-based mimicry through score distillation H Xue, C Liang, X Wu, Y Chen The Twelfth International Conference on Learning Representations, 2023 | 5 | 2023 |
| Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks X Wu, J Zhang, Y Hua, B Lyu, H Wang, T Song, H Guan arXiv preprint arXiv:2405.19931, 2024 | | 2024 |
| CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion X Wu, Y Hua, C Liang, J Zhang, H Wang, T Song, H Guan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | | 2024 |
