DepthVanish: Optimizing Adversarial Interval Structures for Stereo-Depth-Invisible Patches
Yun Xing, Yue Cao, Nhat Chung, Jie Zhang, Ivor Tsang, Ming-Ming Cheng, Yang Liu, Lei Ma, Qing Guo
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 work has shown that repeating optimized textures within patches can effectively mislead stereo depth estimation in digital settings, i.e., when digitally inserted into images. However, our research reveals that these naively repeated texture structures perform poorly in physical world implementations, limiting their practical utility for testing depth estimation systems. In this work, for the first time, we discover that introducing regular intervals between repeated textures, creating a striped structure, significantly enhances physical-world effectiveness. Through extensive experimentation, we analyze how variations of this novel structure influence attack performance. Based on these insights, we develop a novel stereo depth attack that jointly optimizes both the striped structure and texture elements. We also discover that binary black and white textures demonstrate substantially higher effectiveness than colorful textures. Our generated adversarial patches can be inserted into any scenes and successfully attack state-of-the-art stereo depth estimation methods and even commercial RGB-D cameras (Intel RealSense) in real-world conditions, demonstrating their practical relevance for security assessment of depth estimation systems.