  In recent times, monocular depth estimation (MDE) has experienced significant
advancements in performance, largely attributed to the integration of
innovative architectures, i.e., convolutional neural networks (CNNs) and
Transformers. Nevertheless, the susceptibility of these models to adversarial
attacks has emerged as a noteworthy concern, especially in domains where safety
and security are paramount. This concern holds particular weight for MDE due to
its critical role in applications like autonomous driving and robotic
navigation, where accurate scene understanding is pivotal. To assess the
vulnerability of CNN-based depth prediction methods, recent work tries to
design adversarial patches against MDE. However, the existing approaches fall
short of inducing a comprehensive and substantially disruptive impact on the
vision system. Instead, their influence is partial and confined to specific
local areas. These methods lead to erroneous depth predictions only within the
overlapping region with the input image, without considering the
characteristics of the target object, such as its size, shape, and position. In
this paper, we introduce a novel adversarial patch named APARATE. This patch
possesses the ability to selectively undermine MDE in two distinct ways: by
distorting the estimated distances or by creating the illusion of an object
disappearing from the perspective of the autonomous system. Notably, APARATE is
designed to be sensitive to the shape and scale of the target object, and its
influence extends beyond immediate proximity. APARATE, results in a mean depth
estimation error surpassing $0.5$, significantly impacting as much as $99\%$ of
the targeted region when applied to CNN-based MDE models. Furthermore, it
yields a significant error of $0.34$ and exerts substantial influence over
$94\%$ of the target region in the context of Transformer-based MDE.
