Summary of Safeguarding Vision-language Models Against Patched Visual Prompt Injectors, by Jiachen Sun and Changsheng Wang and Jiongxiao Wang and Yiwei Zhang and Chaowei Xiao
Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors
by Jiachen Sun, Changsheng Wang, Jiongxiao Wang, Yiwei Zhang, Chaowei Xiao
First submitted to arxiv on: 17 May 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 language models are becoming increasingly prominent in artificial intelligence, with a focus on multimodality as the next frontier. Vision-language models (VLMs) are at the forefront of this advancement, combining visual and textual data for enhanced understanding and interaction. However, this integration also enlarges the attack surface. In this paper, we propose to address patched visual prompt injection attacks, where adversaries exploit adversarial patches to generate target content in VLMs. Our investigation reveals that patched adversarial prompts exhibit sensitivity to pixel-wise randomization, a trait that remains robust even against adaptive attacks designed to counteract such defenses. We introduce SmoothVLM, a defense mechanism rooted in smoothing techniques, specifically tailored to protect VLMs from the threat of patched visual prompt injectors. Our framework significantly lowers the attack success rate while achieving high context recovery rates for benign images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that large language models are secure and can’t be tricked by fake pictures or texts. These models are very good at understanding what they see, but hackers could use this to make the models do things they don’t want them to do. The researchers found a way to stop these hackers from getting away with it. They created a special tool called SmoothVLM that makes sure the models can still understand pictures and texts, even when someone tries to trick them. This keeps the models safe and helps us use them for good things like recognizing objects or translating languages. |
Keywords
» Artificial intelligence » Prompt
