LPF-Defense: 3D adversarial defense based on frequency analysis.

ABSTRACT: The 3D point clouds are increasingly being used in various application including safety-critical fields. It has recently been demonstrated that deep neural networks can successfully process 3D point clouds. However, these deep networks can be misclassified via 3D adversarial attacks intentionality designed to perturb some point cloud's features. These misclassifications may be due to the network's overreliance on features with unnecessary information in training sets. As such, identifying the features used by deep classifiers and removing features with unnecessary information from the training data can improve network's robustness against adversarial attacks. In this paper, the LPF-Defense framework is proposed to discard this unnecessary information from the training data by suppressing t

SUBMITTER: Naderi H 

PROVIDER: S-EPMC9901796 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature