Poster: Unveiling the Impact of Patch Placement: Adversarial Patch Attacks on Monocular Depth Estimation

Engineering notes

It is known that the targeted patch outperforms the adjacent and remote patch that is placed beyond the target object, when it comes to an attack success rate. In this paper, we experimentally confirm that the patch placement significantly affects the attack success rates, particularly in specific regions.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

For autonomous driving systems, cameras and LiDAR sensors are necessary devices that provide precise depth information by which positions and sizes of objects can be identified. Moreover, recent advances in deep learning have extended their capabilities to include monocular camera setup for depth estimation. Compared with the conventional devices like LiDAR or stereo cameras for the depth estimation, the monocular camera enables to estimate depths with a low cost. It is known that the depth estimation models for the monocular camera are vulnerable to adversarial examples. However, most adversarial attacks against the monocular depth estimation have been conducted with targeted patches that are placed on a target object. It is known that the targeted patch outperforms the adjacent and remote patch that is placed beyond the target object, when it comes to an attack success rate. However, the adjacent and remote patch would provide high flexibility in patch placement, as it can be placed beyond the target object's scope. In this paper, we experimentally confirm that the patch placement significantly affects the attack success rates, particularly in specific regions.

Links and sources

• Official / arXiv page

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