Summary of Pip: Detecting Adversarial Examples in Large Vision-language Models Via Attention Patterns Of Irrelevant Probe Questions, by Yudong Zhang et al.
PIP: Detecting Adversarial Examples in Large Vision-Language Models via Attention Patterns of Irrelevant Probe Questions
by Yudong Zhang, Ruobing Xie, Jiansheng Chen, Xingwu Sun, Yu Wang
First submitted to arxiv on: 8 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 A novel method, called PIP, is proposed to detect adversarial examples in Large Vision-Language Models (LVLMs). LVLMs have shown impressive multimodal capabilities, but are vulnerable to robustness issues due to the use of well-designed adversarial examples. The authors discover that LVLMs exhibit regular attention patterns for clean images when presented with probe questions. PIP utilizes these attention patterns to distinguish between adversarial and clean examples by analyzing the attention pattern of a randomly selected irrelevant probe question. This approach requires only one additional inference step, achieving high recall (over 98%) and precision (over 90%) even under black-box attacks and open dataset scenarios. The PIP method sheds light on deeper understanding and introspection within LVLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LVLMs are super smart computers that can understand images and text together! But, they have a problem: bad guys can trick them by making fake examples. To fix this, researchers found a way to use regular patterns in the computer’s attention (where it focuses on different parts of an image) to detect these fake examples. They call this method PIP, which is like a special filter that helps the computer tell real images from fake ones. It works really well, even when the bad guys try to trick it with tricky questions and pictures! This new way of understanding how LVLMs work might help make them even better at doing cool things like recognizing objects in photos. |
Keywords
» Artificial intelligence » Attention » Inference » Precision » Recall
