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) and consequently lack generalizability across various multimodal tasks. To address this limitation, we propose a dual adversarial fine-tuning framework that jointly optimizes visual and semantic supervision signals from two modalities, enhancing model robustness while generalizing across multiple downstream tasks. The proposed framework comprises two core components, i.e., $\t...
| Authors | Sibo Wang, Jie Zhang, Shiguang Shan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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