Matthew Watson matthew.s.watson@durham.ac.uk
Postdoctoral Research Associate
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
Watson, Matthew; Al Moubayed, Noura
Authors
Dr Noura Al Moubayed noura.al-moubayed@durham.ac.uk
Associate Professor
Abstract
Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks has shown the ease of designing samples to mislead a model into making incorrect predictions. In this work, we propose a model agnostic explainability-based method for the accurate detection of adversarial samples on two datasets with different complexity and properties: Electronic Health Record (EHR) and chest X-ray (CXR) data. On the MIMIC-III and Henan-Renmin EHR datasets, we report a detection accuracy of 77% against the Longitudinal Adversarial Attack. On the MIMIC-CXR dataset, we achieve an accuracy of 88%; significantly improving on the state of the art of adversarial detection in both datasets by over 10% in all settings. We propose an anomaly detection based method using explainability techniques to detect adversarial samples which is able to generalise to different attack methods without a need for retraining.
Citation
Watson, M., & Al Moubayed, N. (2021). Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning. . https://doi.org/10.1109/icpr48806.2021.9412560
Conference Name | The 25th International Conference on Pattern Recognition (ICPR2020) |
---|---|
Conference Location | Milan, Italy |
Start Date | Jan 10, 2021 |
End Date | Jan 15, 2021 |
Acceptance Date | Oct 11, 2020 |
Online Publication Date | May 5, 2021 |
Publication Date | 2021 |
Deposit Date | Oct 11, 2020 |
Publicly Available Date | Oct 13, 2020 |
Series ISSN | 1051-4651 |
DOI | https://doi.org/10.1109/icpr48806.2021.9412560 |
Related Public URLs | https://www.micc.unifi.it/icpr2020/ |
Files
Accepted Conference Proceeding
(1.2 Mb)
PDF
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
You might also like
Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention
(2022)
Conference Proceeding
Towards Graph Representation Learning Based Surgical Workflow Anticipation
(2022)
Conference Proceeding
Does lossy image compression affect racial bias within face recognition?
(2022)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search