  Recent advances of deep learning have brought exceptional performance on many
computer vision tasks such as semantic segmentation and depth estimation.
However, the vulnerability of deep neural networks towards adversarial examples
have caused grave concerns for real-world deployment. In this paper, we present
to the best of our knowledge the first systematic study of adversarial attacks
on monocular depth estimation, an important task of 3D scene understanding in
scenarios such as autonomous driving and robot navigation. In order to
understand the impact of adversarial attacks on depth estimation, we first
define a taxonomy of different attack scenarios for depth estimation, including
non-targeted attacks, targeted attacks and universal attacks. We then adapt
several state-of-the-art attack methods for classification on the field of
depth estimation. Besides, multi-task attacks are introduced to further improve
the attack performance for universal attacks. Experimental results show that it
is possible to generate significant errors on depth estimation. In particular,
we demonstrate that our methods can conduct targeted attacks on given objects
(such as a car), resulting in depth estimation 3-4x away from the ground truth
(e.g., from 20m to 80m).
