New adversarial attack targets SAM3 image segmentation models

Researchers have developed Universal Concept Disruption (UCD), a novel adversarial attack specifically designed to target SAM3 image segmentation models. UCD learns a single image perturbation that can disrupt the model's ability to accurately identify and segment concepts across various datasets. This attack method consistently outperforms existing baselines, significantly reducing segmentation accuracy and concept grounding performance. The learned perturbations also demonstrate transferability to newer versions of SAM3 and even video inference without requiring re-optimization. AI

IMPACT This research highlights potential vulnerabilities in advanced image segmentation models, necessitating further work on adversarial robustness for AI systems.

RANK_REASON The cluster contains an academic paper detailing a new method for attacking AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Lvis
• OpenImages
• PhraseCut
• RefCOCO+
• SACo-Gold
• SAM3
• SAM3.1
• Universal Concept Disruption

• paper
• safety

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