  Emergence of the utility of 3D point cloud data in safety-critical vision
tasks (e.g., ADAS) urges researchers to pay more attention to the robustness of
3D representations and deep networks. To this end, we develop an attack and
defense scheme, dedicated to 3D point cloud data, for preventing 3D point
clouds from manipulated as well as pursuing noise-tolerable 3D representation.
A set of novel 3D point cloud attack operations are proposed via pointwise
gradient perturbation and adversarial point attachment / detachment. We then
develop a flexible perturbation-measurement scheme for 3D point cloud data to
detect potential attack data or noisy sensing data. Notably, the proposed
defense methods are even effective to detect the adversarial point clouds
generated by a proof-of-concept attack directly targeting the defense.
Transferability of adversarial attacks between several point cloud networks is
addressed, and we propose an momentum-enhanced pointwise gradient to improve
the attack transferability. We further analyze the transferability from
adversarial point clouds to grid CNNs and the inverse. Extensive experimental
results on common point cloud benchmarks demonstrate the validity of the
proposed 3D attack and defense framework.
