通过优化纹理间隔结构，提升物理世界中立体深度欺骗攻击效果
DepthVanish: Optimizing Adversarial Interval Structures for Stereo-Depth-Invisible Patches
- 设计带规律间隔的纹理网格结构，增强对抗性扰动
- 在真实场景中成功欺骗RAFT-Stereo、STTR及RealSense等系统
- 适用于自动驾驶与机器人系统的安全压力测试
立体深度估计在自动驾驶和机器人中至关重要，误差可能导致严重后果。针对立体深度估计的对抗攻击可提前揭示系统漏洞。以往研究发现，重复优化纹理能在数字环境中有效误导立体深度估计，但本研究首次发现，此类简单重复纹理在物理贴片部署时表现不佳，限制了其实际应用。我们提出，通过在重复纹理中引入规则间隔，形成网格结构，显著提升攻击效果。经大量实验分析该结构变化对对抗性能的影响，进而提出联合优化间隔与纹理元素的新方法。生成的对抗贴片可嵌入任意场景，成功攻击不同范式的先进立体深度模型（RAFT-Stereo、STTR），且在真实环境中有效欺骗商用RGB-D相机（Intel RealSense），验证了其在立体系统安全评估中的实用价值。代码已开源：https://github.com/WiNiN42/DepthVanish
原文摘要 · Abstract (English)
Stereo depth estimation is a critical task in autonomous driving and robotics, where inaccuracies (such as misidentifying nearby objects as distant) can lead to dangerous situations. Adversarial attacks against stereo depth estimation can help reveal vulnerabilities before deployment. Previous works have shown that repeating optimized textures can effectively mislead stereo depth estimation in digital settings. However, our research reveals that these naively repeated textures perform poorly in physical implementations, i.e., when deployed as patches, limiting their practical utility for stress-testing stereo depth estimation systems. In this work, for the first time, we discover that introducing regular intervals among the repeated textures, creating a grid structure, significantly enhances the patch's attack performance. Through extensive experimentation, we analyze how variations of this novel structure influence the adversarial effectiveness. Based on these insights, we develop a novel stereo depth attack that jointly optimizes both the interval structure and texture elements. Our generated adversarial patches can be inserted into any scenes and successfully attack advanced stereo depth estimation methods of different paradigms, i.e., RAFT-Stereo and STTR. Most critically, our patch can also attack commercial RGB-D cameras (Intel RealSense) in real-world conditions, demonstrating their practical relevance for security assessment of stereo systems. The code is officially released at: https://github.com/WiWiN42/DepthVanish
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