Interpreting and Improving Adversarial Robustness of Deep Neural Networks With Neuron Sensitivity.
IEEE Trans Image Process
; 30: 1291-1304, 2021.
Article
em En
| MEDLINE
| ID: mdl-33290221
ABSTRACT
Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Despite the potential risk they bring, adversarial examples are also valuable for providing insights into the weakness and blind-spots of DNNs. Thus, the interpretability of a DNN in the adversarial setting aims to explain the rationale behind its decision-making process and makes deeper understanding which results in better practical applications. To address this issue, we try to explain adversarial robustness for deep models from a new perspective of neuron sensitivity which is measured by neuron behavior variation intensity against benign and adversarial examples. In this paper, we first draw the close connection between adversarial robustness and neuron sensitivities, as sensitive neurons make the most non-trivial contributions to model predictions in the adversarial setting. Based on that, we further propose to improve adversarial robustness by stabilizing the behaviors of sensitive neurons. Moreover, we demonstrate that state-of-the-art adversarial training methods improve model robustness by reducing neuron sensitivities, which in turn confirms the strong connections between adversarial robustness and neuron sensitivity. Extensive experiments on various datasets demonstrate that our algorithm effectively achieves excellent results. To the best of our knowledge, we are the first to study adversarial robustness using neuron sensitivities.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Aprendizado Profundo
/
Modelos Neurológicos
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
IEEE Trans Image Process
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article