Universal adversarial attacks on deep neural networks for medical image classification.

ABSTRACT:

Background

Deep neural networks (DNNs) are widely investigated in medical image classification to achieve automated support for clinical diagnosis. It is necessary to evaluate the robustness of medical DNN tasks against adversarial attacks, as high-stake decision-making will be made based on the diagnosis. Several previous studies have considered simple adversarial attacks. However, the vulnerability of DNNs to more realistic and higher risk attacks, such as universal adversarial perturbation (UAP), which is a single perturbation that can induce DNN failure in most classification tasks has not been evaluated yet.

Methods

We focus on three representative DNN-based medical image classification tasks (i.e., skin cancer, referable diabetic retinopathy, and pneumonia classificati

SUBMITTER: Hirano H 

PROVIDER: S-EPMC7792111 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature