  We study the effect of adversarial perturbations on the task of monocular
depth prediction. Specifically, we explore the ability of small, imperceptible
additive perturbations to selectively alter the perceived geometry of the
scene. We show that such perturbations can not only globally re-scale the
predicted distances from the camera, but also alter the prediction to match a
different target scene. We also show that, when given semantic or instance
information, perturbations can fool the network to alter the depth of specific
categories or instances in the scene, and even remove them while preserving the
rest of the scene. To understand the effect of targeted perturbations, we
conduct experiments on state-of-the-art monocular depth prediction methods. Our
experiments reveal vulnerabilities in monocular depth prediction networks, and
shed light on the biases and context learned by them.
