# Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch Attack Source: [Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch Attack]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:14:35.342Z Authors: Naufal Suryanto, Andro Aprila Adiputra, Ahmada Yusril Kadiptya, Yongsu Kim, Howon Kim Published: 2024-08-27T08:48:21.000Z ## Abstract Monocular depth estimation (MDE) and semantic segmentation (SS) are crucial for the navigation and environmental interpretation of many autonomous driving systems. However, their vulnerability to practical adversarial attacks is a significant concern. This paper presents a novel adversarial attack using practical patches that mimic manhole covers to deceive MDE and SS models. The goal is to cause these systems to misinterpret scenes, leading to false detections of near obstacles or non-passable objects. We use Depth Planar Mapping to precisely position these patches on road surfaces, enhancing the attack's effectiveness. Our experiments show that these adversarial patches cause a 43% relative error in MDE and achieve a 96% attack success rate in SS. These patches create affected error regions over twice their size in MDE and approximately equal to their size in SS. Our studies also confirm the patch's effectiveness in physical simulations, the adaptability of the patches across different target models, and the effectiveness of our proposed modules, highlighting their practical implications. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()