  Monocular Depth Estimation (MDE) plays a vital role in applications such as
autonomous driving. However, various attacks target MDE models, with physical
attacks posing significant threats to system security. Traditional adversarial
training methods, which require ground-truth labels, are not directly
applicable to MDE models that lack ground-truth depth. Some self-supervised
model hardening techniques (e.g., contrastive learning) overlook the domain
knowledge of MDE, resulting in suboptimal performance. In this work, we
introduce a novel self-supervised adversarial training approach for MDE models,
leveraging view synthesis without the need for ground-truth depth. We enhance
adversarial robustness against real-world attacks by incorporating
L_0-norm-bounded perturbation during training. We evaluate our method against
supervised learning-based and contrastive learning-based approaches
specifically designed for MDE. Our experiments with two representative MDE
networks demonstrate improved robustness against various adversarial attacks,
with minimal impact on benign performance.
