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Authors: Cheng You-Wei and Wang Sheng-De

Affiliation: Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

Keyword(s): Adversarial Attack, Adversarial Defense, Reactive Defense, Data Pre-processing, Deep Learning.

Abstract: Mainstream deep learning algorithms have been shown vulnerable to adversarial attacks - the deep models could be misled by adding small unnoticeable perturbations to the original input image. These attacks could pose security challenges in real-world applications. The paper focuses on how to defend against an adversarial patch attack that confines such noises within a small and localized patch area. We will discuss how an adversarial sample affects the classifier output from the perspective of a deep model by visualizing its saliency map. On the basis of our baseline method: Local Gradients Smoothing, we further design two methods called Saliency-map-based Local Gradients Smoothing and Weighted Local Gradients Smoothing, integrating saliency maps with local gradient maps to accurately locate a possible attacked region and perform smoothing accordingly. Experimental results show that our proposed method could reduce the probability of false smoothing and increase the overall accuracy significantly. (More)

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Paper citation in several formats:
You-Wei, C. and Sheng-De, W. (2022). Enhanced Local Gradient Smoothing: Approaches to Attacked-region Identification and Defense. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 254-263. DOI: 10.5220/0010825400003116

@conference{icaart22,
author={Cheng You{-}Wei. and Wang Sheng{-}De.},
title={Enhanced Local Gradient Smoothing: Approaches to Attacked-region Identification and Defense},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={254-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010825400003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Enhanced Local Gradient Smoothing: Approaches to Attacked-region Identification and Defense
SN - 978-989-758-547-0
IS - 2184-433X
AU - You-Wei, C.
AU - Sheng-De, W.
PY - 2022
SP - 254
EP - 263
DO - 10.5220/0010825400003116
PB - SciTePress

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