Features Normalisation and Standardisation (FNS): An Unsupervised Approach for Detecting Adversarial Attacks for Medical Images

Sreenivasan Mohandas, Naresh Manwani

2023

Abstract

Deep learning systems have shown state-of-the-art performance in clinical prediction tasks. However, current research suggests that cleverly produced hostile images can trick these systems. Deep learning-based medical image classification algorithms have been questioned regarding their practical deployment. To address this problem, we provide an unsupervised learning technique for detecting adversarial attacks on medical images. Without identifying the attackers or reducing classification performance, our suggested strategy FNS (Features Normalization and Standardization), can detect adversarial attacks more effectively than earlier methods.

Paper Citation

in Harvard Style

Mohandas S. and Manwani N. (2023). Features Normalisation and Standardisation (FNS): An Unsupervised Approach for Detecting Adversarial Attacks for Medical Images. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 140-145. DOI: 10.5220/0011624000003393

in Bibtex Style

@conference{icaart23,
author={Sreenivasan Mohandas and Naresh Manwani},
title={Features Normalisation and Standardisation (FNS): An Unsupervised Approach for Detecting Adversarial Attacks for Medical Images},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={140-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011624000003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Features Normalisation and Standardisation (FNS): An Unsupervised Approach for Detecting Adversarial Attacks for Medical Images
SN - 978-989-758-623-1
AU - Mohandas S.
AU - Manwani N.
PY - 2023
SP - 140
EP - 145
DO - 10.5220/0011624000003393