New CamoShift framework attacks visible-infrared object detectors

Researchers have developed CamoShift, a novel adversarial framework designed to attack visible-infrared object detectors. This method combines visual camouflage with object-level infrared shifting to disrupt cross-modal spatial alignment and fusion processes. CamoShift aims to achieve a superior balance between attack effectiveness and visual stealth, outperforming existing physical attack methods. AI

IMPACT Introduces novel adversarial techniques for visible-infrared object detection, potentially impacting robustness testing and security.

RANK_REASON The item is an academic paper detailing a new adversarial framework for computer vision research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• alphaXiv
• arXiv
• CamoShift
• CatalyzeX Code Finder for Papers
• CORE Recommender
• DagsHub
• GitHub
• Gotit.pub
• HarAdv loss
• Hugging Face
• Influence Flower
• Object-level Spatial Decoupling Module
• RGB-IR adapter
• ScienceCast
• Semantic Camouflage Module

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →