  Monocular Depth Estimation (MDE) is a critical component in applications such
as autonomous driving. There are various attacks against MDE networks. These
attacks, especially the physical ones, pose a great threat to the security of
such systems. Traditional adversarial training method requires ground-truth
labels hence cannot be directly applied to self-supervised MDE that does not
have ground-truth depth. Some self-supervised model hardening techniques (e.g.,
contrastive learning) ignore the domain knowledge of MDE and can hardly achieve
optimal performance. In this work, we propose a novel adversarial training
method for self-supervised MDE models based on view synthesis without using
ground-truth depth. We improve adversarial robustness against physical-world
attacks using L0-norm-bounded perturbation in training. We compare our method
with supervised learning based and contrastive learning based methods that are
tailored for MDE. Results on two representative MDE networks show that we
achieve better robustness against various adversarial attacks with nearly no
benign performance degradation.
