Jarvis1111/InternVL2-8B-RobustVLGuard

🚀 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.

  

• 🛡️ 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.

  

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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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