发表机构
Beijing University of Technology; Peking University; National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences; Intelligent Science & Technology Academy of CASIC（北京工业大学; 北京大学; 中国科学院软件研究所空间集成信息系统全国重点实验室; 中国航天科工集团智能科技研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出CamoShift对抗框架，通过语义伪装和红外目标级移位打破跨模态对齐，平衡攻击强度与视觉隐蔽性，实现可见光-红外目标检测的高效攻击。
AI 中文摘要
可见光-红外目标检测器用于在具有挑战性的光照和天气条件下进行稳健感知。当前的物理攻击将显眼的补丁应用于空间对齐的目标区域，这些补丁对人类观察者来说很明显。同时，这些方法大多只扰动对齐区域内的外观，没有明确针对模态之间的对应关系或融合过程。在本文中，我们提出了CamoShift，一个用于可见光-红外目标检测的对抗性框架。通过将视觉伪装与目标级红外移位相结合，CamoShift打破了跨模态空间对齐并扰乱了融合。具体来说，语义伪装模块（SCM）生成一个隐蔽的伪装补丁，可以附着在宿主目标上，并通过RGB-IR适配器在红外分支中保持其有效性。目标级空间解耦模块（OSDM）以尺度感知的方式移动红外目标证据，从而打破目标级对应关系并扰乱跨模态融合。然后，谐波对抗损失（HarAdv损失）在优化过程中进一步平衡攻击强度和视觉隐蔽性。据我们所知，我们是第一个在可见光-红外目标检测中同时针对视觉隐蔽性和攻击成功率的。大量实验结果表明，CamoShift在攻击有效性和视觉隐蔽性之间实现了优越的平衡。代码和模型将在GitHub上提供。
英文摘要
Visible-infrared object detectors are used for robust perception under challenging illumination and weather conditions. Current physical attacks apply conspicuous patches to spatially aligned target regions, which are noticeable to human observers. Meanwhile, most of these methods only perturb the appearance within the aligned region, without explicitly targeting the correspondence between modalities or the fusion process. In this paper, we propose CamoShift, an adversarial framework for visible-infrared object detection. By combining visual camouflage with object-level infrared shifting, CamoShift breaks cross-modal spatial alignment and disrupts fusion. Specifically, the Semantic Camouflage Module (SCM) generates a stealthy camouflaged patch that can be attached to the host object and maintains its effectiveness in the infrared branch through an RGB-IR adapter. The Object-level Spatial Decoupling Module (OSDM) shifts the infrared target evidence in a scale-aware manner, so as to break object-level correspondence and disrupt cross-modal fusion. Then, the Harmonic Adversarial loss (HarAdv loss) further balances attack strength and visual stealth during optimization. To the best of our knowledge, we are the first to target both visual stealthiness and attack success in visible-infrared object detection. Extensive experimental results show that CamoShift achieves a superior balance between attack effectiveness and visual stealth. Code and models will be available on GitHub.
