3D-Adv: Black-Box Adversarial Attacks against Deep Learning Models through 3D Sensors

The combination of deep learning techniques and commercial 3D sensors reveal a bright future as they provide a low cost and convenient method to collect and analyze depth information from the environment for various applications ranging from industrial modeling to mobile face recognition. Despite the abundant research devoted to the development of more accurate, flexible and efficient machine learning schemes as well as 3D sensors, security concerns related to these techniques remain largely untouched. In this paper, we propose a novel adversarial attack against this combination by showing that deep learning models with popular 3D sensors may misclassify real objects in the physical environment. Comparing to the existing attack algorithms against deep learning models developed for 3D data analysis that only consider digital point cloud data and single deep learning model, our attacks target popular commercial 3D sensors combined with various deep learning schemes in the black-box setting. Experimental results demonstrate that our 3D printed adversarial objects stay effective after scanned by a 3D sensor.