| 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 | 97 | 2023 |
| Mist: Towards improved adversarial examples for diffusion models C Liang, X Wu arXiv preprint arXiv:2305.12683, 2023 | 33 | 2023 |
| Understanding and improving adversarial attacks on latent diffusion model B Zheng, C Liang, X Wu, Y Liu arXiv preprint arXiv:2310.04687, 2023 | 19 | 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 | 17 | 2023 |
| 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 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR …, 2024 | 3 | 2024 |
| Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data X Wu, J Zhang, S Wu arXiv preprint arXiv:2410.03039, 2024 | | 2024 |
| 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 |
Chumeng LiangUniversity of Southern CaliforniaVerified email at usc.edu
