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Authors: C. Lee ; D. Seok ; D. Shim and R. Park

Affiliation: Dept. of Electronical and Electronic Engineering, Yonsei University, Republic of Korea

Keyword(s): CNN-Based Classifier, Modified kNN Classifier, Adversarial Attack, Output Vector Space.

Abstract: Although CNN-based classifiers have been successfully applied to many pattern classification problems, they suffer from adversarial attacks. Slightly modified images can be classified as completely different classes. It has been reported that CNN-based classifiers tend to construct decision boundaries close to training samples. In order to mitigate this problem, we applied modified kNN classifiers in the output vector space of CNN-based classifiers. Experimental results show that the proposed method noticeably reduced the classification error caused by adversarial attacks.

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Paper citation in several formats:
Lee, C.; Seok, D.; Shim, D. and Park, R. (2023). Modified kNN Classifier in the Output Vector Space for Robust Performance Against Adversarial Attack. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 443-449. DOI: 10.5220/0011735800003411

@conference{icpram23,
author={C. Lee. and D. Seok. and D. Shim. and R. Park.},
title={Modified kNN Classifier in the Output Vector Space for Robust Performance Against Adversarial Attack},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2023},
pages={443-449},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011735800003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Modified kNN Classifier in the Output Vector Space for Robust Performance Against Adversarial Attack
SN - 978-989-758-626-2
IS - 2184-4313
AU - Lee, C.
AU - Seok, D.
AU - Shim, D.
AU - Park, R.
PY - 2023
SP - 443
EP - 449
DO - 10.5220/0011735800003411
PB - SciTePress

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