  Thanks to the excellent learning capability of deep convolutional neural
networks (CNN), monocular depth estimation using CNNs has achieved great
success in recent years. However, depth estimation from a monocular image alone
is essentially an ill-posed problem, and thus, it seems that this approach
would have inherent vulnerabilities. To reveal this limitation, we propose a
method of adversarial patch attack on monocular depth estimation. More
specifically, we generate artificial patterns (adversarial patches) that can
fool the target methods into estimating an incorrect depth for the regions
where the patterns are placed. Our method can be implemented in the real world
by physically placing the printed patterns in real scenes. We also analyze the
behavior of monocular depth estimation under attacks by visualizing the
activation levels of the intermediate layers and the regions potentially
affected by the adversarial attack.
