Summary of Darksam: Fooling Segment Anything Model to Segment Nothing, by Ziqi Zhou et al.
DarkSAM: Fooling Segment Anything Model to Segment Nothing
by Ziqi Zhou, Yufei Song, Minghui Li, Shengshan Hu, Xianlong Wang, Leo Yu Zhang, Dezhong Yao, Hai Jin
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The Segment Anything Model (SAM) has shown impressive generalization capabilities, but its vulnerabilities to universal adversarial perturbation (UAP) have not been thoroughly explored. This paper proposes DarkSAM, a prompt-free universal attack framework designed specifically against SAM. The authors develop a two-pronged approach: a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. First, they divide the output of SAM into foreground and background, then design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. In the spatial domain, DarkSAM disrupts the semantics of both the foreground and background to confuse SAM. Additionally, in the frequency domain, it distorts high-frequency components (texture information) to enhance attack effectiveness. Experimental results on four datasets for SAM and its variants demonstrate DarkSAM’s powerful attack capabilities and transferability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new kind of attack has been developed that can trick a popular AI model called Segment Anything Model (SAM). This attack, called DarkSAM, is designed to make SAM unable to recognize objects in images. The researchers who created DarkSAM divided the output of SAM into two parts: what’s important and what’s not. Then, they came up with a plan to create a fake target that looks like what SAM would normally see. In one way, they changed the important parts of the image to confuse SAM. In another way, they messed with the texture of the image to make it harder for SAM to recognize objects. When tested on different images and datasets, DarkSAM was very good at fooling SAM. |
Keywords
» Artificial intelligence » Generalization » Prompt » Sam » Semantics » Transferability
