  This paper proposes an adversarial attack method to deep neural networks
(DNNs) for monocular depth estimation, i.e., estimating the depth from a single
image. Single image depth estimation has improved drastically in recent years
due to the development of DNNs. However, vulnerabilities of DNNs for image
classification have been revealed by adversarial attacks, and DNNs for
monocular depth estimation could contain similar vulnerabilities. Therefore,
research on vulnerabilities of DNNs for monocular depth estimation has spread
rapidly, but many of them assume white-box conditions where inside information
of DNNs is available, or are transferability-based black-box attacks that
require a substitute DNN model and a training dataset. Utilizing Evolutionary
Multi-objective Optimization, the proposed method in this paper analyzes DNNs
under the black-box condition where only output depth maps are available. In
addition, the proposed method does not require a substitute DNN that has a
similar architecture to the target DNN nor any knowledge about training data
used to train the target model. Experimental results showed that the proposed
method succeeded in attacking two DNN-based methods that were trained with
indoor and outdoor scenes respectively.
