基于3D建模的热图像行人检测器的物理对抗样本
Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling
浏览论文内容
中文总结 AI 辅助
该研究基于3D建模制作红外对抗服装，针对YOLOv9等热图像行人检测器实现高攻击成功率，且具有良好的可迁移性。
中文摘要 AI 辅助
红外热成像检测广泛应用于自动驾驶、医疗AI等领域，但其安全性直到近期才受到关注。本文提出一种旨在现实场景中规避热成像行人检测器的红外对抗服装，其设计基于3D建模，相比2D建模更易模拟接近现实的多角度场景。我们基于对抗样本技术优化了3D服装的黑色贴片布局模式，并使用气凝胶制作了物理对抗服装，具体思路是在服装内侧特定位置、以特定方向粘贴一组方形气凝胶贴片，这些贴片会在热图像中显示为黑色方块。为增强真实感，我们提出一种利用真实红外照片构建红外3D模型的方法，并为3D模型开发纹理图，以模拟不同时间和位置下的红外特性变化。在物理攻击实验中，针对YOLOv9模型，我们的方法在室内实现了80.11%的攻击成功率，室外为76.85%；相比之下，随机放置贴片的攻击成功率低得多，室内仅为26.53%，室外为23.03%。此外，该对抗服装通过集成攻击方法对未知检测器表现出良好的可迁移性，证明了所提方法的有效性。
英文摘要
Thermal Infrared detection is widely used in autonomous driving, medical AI, etc., but its security has only attracted attention recently. We propose infrared adversarial clothing designed to evade thermal person detectors in real-world scenarios. The design of the adversarial clothing is based on 3D modeling, which makes it easier to simulate multiangle scenes near the real world compared to 2D modeling. We optimized the black patch layout pattern of 3D clothing based on the adversarial example technique and made physical adversarial clothing using the aerogel. The idea is to paste a set of square aerogel patches, which display black squares in thermal images, in the inner side of clothing at specific locations with specific orientations. To enhance realism, we propose a method to build infrared 3D models with real infrared photos and develop texture maps for 3D models to simulate varied infrared characteristics over time and location. In physical attacks, we achieved an attack success rate of 80.11\% indoors and 76.85\% outdoors against YOLOv9. In contrast, randomly placed patches yielded much lower success rates (26.53\% indoors and 23.03\% outdoors). The adversarial clothing also showed good transferability to unknown detectors with an ensemble attack method, demonstrating the effectiveness of our approach.
发表机构
- Tsinghua University（清华大学）
- Chinese Institute for Brain Research (CIBR)（中国脑科学研究院）
机构由 AI 辅助整理，请以论文原文为准。
