README.md

---
datasets:
- Jarvis1111/RobustVLGuard
license: mit
pipeline_tag: image-text-to-text
library_name: transformers
base_model:
- OpenGVLab/InternVL2-8B
---

# 🚀 Safeguarding Vision-Language Models: Mitigating Vulnerabilities to Gaussian Noise in Perturbation-based Attacks

Welcome! This repository hosts the official implementation of our paper, **"Safeguarding Vision-Language Models: Mitigating Vulnerabilities to Gaussian Noise in Perturbation-based Attacks."**

Paper link: arxiv.org/abs/2504.01308

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## 🌟 What’s New?

We propose state-of-the-art solutions to enhance the robustness of Vision-Language Models (VLMs) against Gaussian noise and adversarial attacks. Key highlights include:

- 🎯 **Robust-VLGuard**: A pioneering multimodal safety dataset covering both aligned and misaligned image-text pair scenarios.

  ![RobustVLGuard](assets/Robust-VLGuard.png)

- 🛡️ **DiffPure-VLM**: A novel defense framework that leverages diffusion models to neutralize adversarial noise by transforming it into Gaussian-like noise, significantly improving VLM resilience.

  ![DiffPure-VLM](assets/DiffPure-VLM.png)

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## ✨ Key Contributions

- 🔍 Conducted a comprehensive vulnerability analysis revealing the sensitivity of mainstream VLMs to Gaussian noise.
- 📚 Developed **Robust-VLGuard**, a dataset designed to improve model robustness without compromising helpfulness or safety alignment.
- ⚙️ Introduced **DiffPure-VLM**, an effective pipeline for defending against complex optimization-based adversarial attacks.
- 📈 Demonstrated strong performance across multiple benchmarks, outperforming existing baseline methods.

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