Jul 2026
Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model
While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulnerable to adversarial attacks, posing significant security risks. Existing defense methods predominantly target single-task scenarios (e.g., zero-shot classification) an...
Sibo Wang, Jie Zhang, Shiguang Shan et al.
· arXiv.org · 0 citations