  In this paper, we investigate the vulnerability of MDE to adversarial
patches. We propose a novel \underline{S}tealthy \underline{A}dversarial
\underline{A}ttacks on \underline{M}DE (SAAM) that compromises MDE by either
corrupting the estimated distance or causing an object to seamlessly blend into
its surroundings. Our experiments, demonstrate that the designed stealthy patch
successfully causes a DNN-based MDE to misestimate the depth of objects. In
fact, our proposed adversarial patch achieves a significant 60\% depth error
with 99\% ratio of the affected region. Importantly, despite its adversarial
nature, the patch maintains a naturalistic appearance, making it inconspicuous
to human observers. We believe that this work sheds light on the threat of
adversarial attacks in the context of MDE on edge devices. We hope it raises
awareness within the community about the potential real-life harm of such
attacks and encourages further research into developing more robust and
adaptive defense mechanisms.
