Summary of Securing Vision-language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks, by Md Zarif Hossain et al.
Securing Vision-Language Models with a Robust Encoder Against Jailbreak and Adversarial Attacks
by Md Zarif Hossain, Ahmed Imteaj
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Vision-Language Models (LVLMs) have achieved significant advancements in vision-language tasks, but they remain vulnerable to adversarial attacks, particularly jailbreak attacks. The proposed defense mechanism, Sim-CLIP+, fine-tunes the CLIP vision encoder using a Siamese architecture to maximize cosine similarity between perturbed and clean samples, providing resilience against adversarial manipulations. This plug-and-play solution can be seamlessly integrated into existing LVLM architectures as a robust vision encoder with minimal computational overhead. Sim-CLIP+ demonstrates effectiveness against various jailbreak techniques and gradient-based adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision-Language Models (LVLMs) are super smart, but they have a problem: they’re easily tricked by bad guys trying to make them say mean or false things. To fix this, scientists created Sim-CLIP+, a clever way to make LVLMs more resistant to these tricks. It works like a special filter that makes sure the model is giving accurate answers and not saying anything silly. This new technology can be easily added to existing models without slowing them down too much. |
Keywords
» Artificial intelligence » Cosine similarity » Encoder
