loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Gabriel R. Machado 1 ; Ronaldo R. Goldschmidt 1 and Eugênio Silva 2

Affiliations: 1 Section of Computer Engineering (SE/8), Military Institute of Engineering (IME), Rio de Janeiro and Brazil ; 2 Computing Center (UComp), State University of West Zone (UEZO), Rio de Janeiro and Brazil

Keyword(s): Artificial Intelligence and Decision Support Systems, Advanced Applications of Neural Networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Deep Neural Networks have been increasingly used in decision support systems, mainly because they are the state-of-the-art algorithms for solving challenging tasks, such as image recognition and classification. However, recent studies have shown these learning models are vulnerable to adversarial attacks, i.e. attacks conducted with images maliciously modified by an algorithm to induce misclassification. Several works have proposed methods for defending against adversarial images, however these defenses have shown to be inefficient, since they have facilitated the understanding of their internal operation by attackers. Thus, this paper proposes a defense called MultiMagNet, which randomly incorporates at runtime multiple defense components, in an attempt to introduce an expanded form of non-deterministic behavior so as to hinder evasions by adversarial attacks. Experiments performed on MNIST and CIFAR-10 datasets prove that MultiMagNet can protect classification models from adversari al images generated by the main existing attacks algorithms. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.167.52.238

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Machado, G.; Goldschmidt, R. and Silva, E. (2019). MultiMagNet: A Non-deterministic Approach based on the Formation of Ensembles for Defending Against Adversarial Images. In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-372-8; ISSN 2184-4984, SciTePress, pages 307-318. DOI: 10.5220/0007714203070318

@conference{iceis19,
author={Gabriel R. Machado. and Ronaldo R. Goldschmidt. and Eugênio Silva.},
title={MultiMagNet: A Non-deterministic Approach based on the Formation of Ensembles for Defending Against Adversarial Images},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2019},
pages={307-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007714203070318},
isbn={978-989-758-372-8},
issn={2184-4984},
}

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - MultiMagNet: A Non-deterministic Approach based on the Formation of Ensembles for Defending Against Adversarial Images
SN - 978-989-758-372-8
IS - 2184-4984
AU - Machado, G.
AU - Goldschmidt, R.
AU - Silva, E.
PY - 2019
SP - 307
EP - 318
DO - 10.5220/0007714203070318
PB - SciTePress