用3D模拟优化对抗贴纸，骗过货车货物占用率检测系统
Adversarial Patch Attacks on Vision-Based Cargo Occupancy Estimation via Differentiable 3D Simulation
- 通过可微分3D渲染优化贴纸纹理，适应光照视角变化
- 在清空伪装成满载场景下攻击成功率高达84.94%
- 首次在真实感3D环境中研究货物占用估计的物理对抗攻击
计算机视觉系统正被广泛应用于现代物流中，用于估算车厢货物占用率以支持规划、调度和计费。尽管高效，这类系统可能面临物理对抗攻击，特别是可打印并放置于内部表面的对抗贴纸。本文研究了基于全仿真3D环境的卷积货物占用分类器遭受此类攻击的可行性。利用Mitsuba 3进行可微分渲染，我们优化了在几何、光照和视角变化下的贴纸纹理，并与2D叠加基线方法对比。实验表明，3D优化贴纸在拒绝服务场景（空到满）中攻击成功率可达84.94%，而在隐藏攻击（满到空）中仍达到30.32%。我们分析了影响攻击成功的因素，讨论了自动化物流链安全的影响，并提出增强物理鲁棒性的方向。据我们所知，这是首个在物理逼真、完全仿真的3D场景中研究货物占用估计对抗贴纸攻击的工作。
原文摘要 · Abstract (English)
Computer vision systems are increasingly adopted in modern logistics operations, including the estimation of trailer occupancy for planning, routing, and billing. Although effective, such systems may be vulnerable to physical adversarial attacks, particularly adversarial patches that can be printed and placed on interior surfaces. In this work, we study the feasibility of such attacks on a convolutional cargo-occupancy classifier using fully simulated 3D environments. Using Mitsuba 3 for differentiable rendering, we optimize patch textures across variations in geometry, lighting, and viewpoint, and compare their effectiveness to a 2D compositing baseline. Our experiments demonstrate that 3D-optimized patches achieve high attack success rates, especially in a denial-of-service scenario (empty to full), where success reaches 84.94 percent. Concealment attacks (full to empty) prove more challenging but still reach 30.32 percent. We analyze the factors influencing attack success, discuss implications for the security of automated logistics pipelines, and highlight directions for strengthening physical robustness. To our knowledge, this is the first study to investigate adversarial patch attacks for cargo-occupancy estimation in physically realistic, fully simulated 3D scenes.
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