Monocular depth estimation (MDE) has advanced significantly, primarily
through the integration of convolutional neural networks (CNNs) and more
recently, Transformers. However, concerns about their susceptibility to
adversarial attacks have emerged, especially in safety-critical domains like
autonomous driving and robotic navigation. Existing approaches for assessing
CNN-based depth prediction methods have fallen short in inducing comprehensive
disruptions to the vision system, often limited to specific local areas. In
this paper, we introduce SSAP (Shape-Sensitive Adversarial Patch), a novel
approach designed to comprehensively disrupt monocular depth estimation (MDE)
in autonomous navigation applications. Our patch is crafted to selectively
undermine MDE in two distinct ways: by distorting estimated distances or by
creating the illusion of an object disappearing from the system's perspective.
Notably, our patch is shape-sensitive, meaning it considers the specific shape
and scale of the target object, thereby extending its influence beyond
immediate proximity. Furthermore, our patch is trained to effectively address
different scales and distances from the camera. Experimental results
demonstrate that our approach induces a mean depth estimation error surpassing
0.5, impacting up to 99% of the targeted region for CNN-based MDE models.
Additionally, we investigate the vulnerability of Transformer-based MDE models
to patch-based attacks, revealing that SSAP yields a significant error of 0.59
and exerts substantial influence over 99% of the target region on these models.

通过引入 SSAP 方法，在自主导航应用中全面干扰 CNN-based MDE 模型的深度估计，实验结果显示该方法对目标区域产生显著错误，并影响了 99% 的区域。