用不重叠的可见光-热成像图案设计抗检测服装，骗过多模态目标检测系统。
Physical Adversarial Clothing Evades Visible-Thermal Detectors via Non-Overlapping RGB-T Pattern
- 提出非重叠RGB-T图案（NORP），用不同材质避免光衰减。
- 在数字与物理世界均实现高成功率攻击，跨架构转移性强。
- 适合研究多模态安全、对抗样本防御的开发者参考。
可见-热成像（RGB-T）目标检测在自动驾驶等场景中至关重要，多模态融合可提升低光照等复杂条件下的性能。然而，物理世界中RGB-T检测器的安全性长期被忽视。本文提出一种新型物理攻击方法，利用带有非重叠RGB-T图案（NORP）的对抗性服装进行全视角（0°–360°）攻击。为模拟真实场景，构建了人体与对抗服装的3D RGB-T模型。NORP采用可见光与热成像材料无重叠设计，避免重叠图案（ORP）导致的光线衰减问题。通过空间离散-连续优化（SDCO）方法优化服装上的NORP。在多种融合架构的RGB-T检测器上系统评估，验证了数字与物理世界中的高攻击成功率。此外，引入融合阶段集成方法，显著提升对抗攻击在未见检测器间的迁移能力。
原文摘要 · Abstract (English)
Visible-thermal (RGB-T) object detection is a crucial technology for applications such as autonomous driving, where multimodal fusion enhances performance in challenging conditions like low light. However, the security of RGB-T detectors, particularly in the physical world, has been largely overlooked. This paper proposes a novel approach to RGB-T physical attacks using adversarial clothing with a non-overlapping RGB-T pattern (NORP). To simulate full-view (0$^{\circ}$--360$^{\circ}$) RGB-T attacks, we construct 3D RGB-T models for human and adversarial clothing. NORP is a new adversarial pattern design using distinct visible and thermal materials without overlap, avoiding the light reduction in overlapping RGB-T patterns (ORP). To optimize the NORP on adversarial clothing, we propose a spatial discrete-continuous optimization (SDCO) method. We systematically evaluated our method on RGB-T detectors with different fusion architectures, demonstrating high attack success rates both in the digital and physical worlds. Additionally, we introduce a fusion-stage ensemble method that enhances the transferability of adversarial attacks across unseen RGB-T detectors with different fusion architectures.
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