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

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ethics.ai (21 July 2026), “Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model,” evidence record 12345, https://ethics.ai/record/12345 (originally published by arXiv).

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