Adversarial Patch Attacks on Monocular Depth Estimation Networks

Understand

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.