  Adversarial attacks against monocular depth estimation (MDE) systems pose
significant challenges, particularly in safety-critical applications such as
autonomous driving. Existing patch-based adversarial attacks for MDE are
confined to the vicinity of the patch, making it difficult to affect the entire
target. To address this limitation, we propose a physics-based adversarial
attack on monocular depth estimation, employing a framework called Attack with
Shape-Varying Patches (ASP), aiming to optimize patch content, shape, and
position to maximize effectiveness. We introduce various mask shapes, including
quadrilateral, rectangular, and circular masks, to enhance the flexibility and
efficiency of the attack. Furthermore, we propose a new loss function to extend
the influence of the patch beyond the overlapping regions. Experimental results
demonstrate that our attack method generates an average depth error of 18
meters on the target car with a patch area of 1/9, affecting over 98\% of the
target area.
