Summary of Region-guided Attack on the Segment Anything Model (sam), by Xiaoliang Liu et al.
Region-Guided Attack on the Segment Anything Model (SAM)
by Xiaoliang Liu, Furao Shen, Jian Zhao
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel attack strategy, called Region-Guided Attack (RGA), designed specifically for the Segment Anything Model (SAM). The goal is to demonstrate exceptional performance across various applications, particularly in autonomous driving and medical imaging. However, SAM is vulnerable to adversarial attacks that can impair its functionality through minor input perturbations. Traditional techniques are often ineffective due to their reliance on global perturbations that overlook spatial nuances. Recent methods have begun to address these challenges but frequently depend on external cues and do not fully leverage structural interdependencies within segmentation processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to attack the Segment Anything Model, which is good at recognizing objects in images. The current defenses against these attacks are not very effective because they don’t consider how the image is structured. This new attack method, called Region-Guided Attack, works by targeting specific parts of the image that SAM relies on. It’s like finding a weak spot and exploiting it to make SAM produce wrong answers. |
Keywords
» Artificial intelligence » Sam
