This research introduces a novel double visual defense technique that significantly enhances vision-language models' resistance to adversarial visual attacks.

• Adversarial Pre-training: Uses large-scale web data to build fundamental defense mechanisms from scratch
• Instruction Tuning: Strengthens robustness through additional adversarial visual instruction tuning
• Superior Protection: Creates more resilient models compared to traditional lightweight fine-tuning approaches
• Security Impact: Directly addresses critical vulnerabilities in AI systems that process both visual and language information

This approach represents a significant advancement for securing multimodal AI systems in high-stakes applications where visual manipulation could lead to security breaches or misinformation.

Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness