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Authors: Sreenivasan Mohandas and Naresh Manwani

Affiliation: Machine Learning Lab, International Institute of Information Technology, Hyderabad, India

Keyword(s): Adversarial Attacks, Adversarial Defenses, Multivariate Gaussian Models, Medical Applications, Features Normalization, Standardization.

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.

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 140-145. DOI: 10.5220/0011624000003393

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Mohandas, S.
AU - Manwani, N.
PY - 2023
SP - 140
EP - 145
DO - 10.5220/0011624000003393
PB - SciTePress

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