Cargando…
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks
Owing the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has become an advanced open source to rapidly and acc...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745979/ https://www.ncbi.nlm.nih.gov/pubmed/33332412 http://dx.doi.org/10.1371/journal.pone.0243963 |
_version_ | 1783624699166588928 |
---|---|
author | Hirano, Hokuto Koga, Kazuki Takemoto, Kazuhiro |
author_facet | Hirano, Hokuto Koga, Kazuki Takemoto, Kazuhiro |
author_sort | Hirano, Hokuto |
collection | PubMed |
description | Owing the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has become an advanced open source to rapidly and accurately detect COVID-19 cases because the need for expert radiologists, who are limited in number, forms a bottleneck for screening. However, thus far, the vulnerability of DNN-based systems has been poorly evaluated, although realistic and high-risk attacks using universal adversarial perturbation (UAP), a single (input image agnostic) perturbation that can induce DNN failure in most classification tasks, are available. Thus, we focus on representative DNN models for detecting COVID-19 cases from chest X-ray images and evaluate their vulnerability to UAPs. We consider non-targeted UAPs, which cause a task failure, resulting in an input being assigned an incorrect label, and targeted UAPs, which cause the DNN to classify an input into a specific class. The results demonstrate that the models are vulnerable to non-targeted and targeted UAPs, even in the case of small UAPs. In particular, the 2% norm of the UAPs to the average norm of an image in the image dataset achieves >85% and >90% success rates for the non-targeted and targeted attacks, respectively. Owing to the non-targeted UAPs, the DNN models judge most chest X-ray images as COVID-19 cases. The targeted UAPs allow the DNN models to classify most chest X-ray images into a specified target class. The results indicate that careful consideration is required in practical applications of DNNs to COVID-19 diagnosis; in particular, they emphasize the need for strategies to address security concerns. As an example, we show that iterative fine-tuning of DNN models using UAPs improves the robustness of DNN models against UAPs. |
format | Online Article Text |
id | pubmed-7745979 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77459792020-12-31 Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks Hirano, Hokuto Koga, Kazuki Takemoto, Kazuhiro PLoS One Research Article Owing the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray computed tomography imaging is being used for effectively screening COVID-19 patients. The development of computer-aided systems based on deep neural networks (DNNs) has become an advanced open source to rapidly and accurately detect COVID-19 cases because the need for expert radiologists, who are limited in number, forms a bottleneck for screening. However, thus far, the vulnerability of DNN-based systems has been poorly evaluated, although realistic and high-risk attacks using universal adversarial perturbation (UAP), a single (input image agnostic) perturbation that can induce DNN failure in most classification tasks, are available. Thus, we focus on representative DNN models for detecting COVID-19 cases from chest X-ray images and evaluate their vulnerability to UAPs. We consider non-targeted UAPs, which cause a task failure, resulting in an input being assigned an incorrect label, and targeted UAPs, which cause the DNN to classify an input into a specific class. The results demonstrate that the models are vulnerable to non-targeted and targeted UAPs, even in the case of small UAPs. In particular, the 2% norm of the UAPs to the average norm of an image in the image dataset achieves >85% and >90% success rates for the non-targeted and targeted attacks, respectively. Owing to the non-targeted UAPs, the DNN models judge most chest X-ray images as COVID-19 cases. The targeted UAPs allow the DNN models to classify most chest X-ray images into a specified target class. The results indicate that careful consideration is required in practical applications of DNNs to COVID-19 diagnosis; in particular, they emphasize the need for strategies to address security concerns. As an example, we show that iterative fine-tuning of DNN models using UAPs improves the robustness of DNN models against UAPs. Public Library of Science 2020-12-17 /pmc/articles/PMC7745979/ /pubmed/33332412 http://dx.doi.org/10.1371/journal.pone.0243963 Text en © 2020 Hirano et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hirano, Hokuto Koga, Kazuki Takemoto, Kazuhiro Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks |
title | Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks |
title_full | Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks |
title_fullStr | Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks |
title_full_unstemmed | Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks |
title_short | Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks |
title_sort | vulnerability of deep neural networks for detecting covid-19 cases from chest x-ray images to universal adversarial attacks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745979/ https://www.ncbi.nlm.nih.gov/pubmed/33332412 http://dx.doi.org/10.1371/journal.pone.0243963 |
work_keys_str_mv | AT hiranohokuto vulnerabilityofdeepneuralnetworksfordetectingcovid19casesfromchestxrayimagestouniversaladversarialattacks AT kogakazuki vulnerabilityofdeepneuralnetworksfordetectingcovid19casesfromchestxrayimagestouniversaladversarialattacks AT takemotokazuhiro vulnerabilityofdeepneuralnetworksfordetectingcovid19casesfromchestxrayimagestouniversaladversarialattacks |