Update README.md

README.md

@@ -1,3 +1,30 @@
----
-license: mit
----
+---
+license: mit
+---
+
+# 🚀 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."**
+
+---
+
+## 🌟 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.
+
+
+- 🛡️ **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.
+
+
+---
+
+## ✨ 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.
+
+---