2 papers
cs.CR2024
Technical Report for ICML 2024 TiFA Workshop MLLM Attack Challenge: Suffix Injection and Projected Gradient Descent Can Easily Fool An MLLM
Yangyang Guo, Ziwei Xu, Xilie Xu +3
This technical report introduces our top-ranked solution that employs two approaches, \ie suffix injection and projected gradient descent (PGD) , to address the TiFA workshop MLLM…
cs.LG2024
Privacy-Preserving Low-Rank Adaptation against Membership Inference Attacks for Latent Diffusion Models
Zihao Luo, Xilie Xu, Feng Liu +3
Low-rank adaptation (LoRA) is an efficient strategy for adapting latent diffusion models (LDMs) on a private dataset to generate specific images by minimizing the adaptation loss.…