loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Jason Jung ; Naveed Akhtar and Ghulam Mubashar Hassan

Affiliation: Department of Computer Science & Software Engineering, The University of Western Australia, Australia

Keyword(s): Adversarial Examples, Adversarial Attacks, Imagenet, Neural Networks, Image Classifiers.

Abstract: The aim of this work is to investigate adversarial examples and look for commonalities and disparities between different adversarial attacks and attacked classifier model behaviours. The research focuses on untargeted, gradient-based attacks. The experiment uses 16 attacks on 4 models and 1000 images. This resulted in 64,000 adversarial examples. The resulting classification predictions of the adversarial examples (adversarial labels) are analysed. It is found that light-weight neural network classifiers are more suspectable to attacks compared to the models with a larger or more complex architecture. It is also observed that similar adversarial attacks against a light-weight model often result in the same adversarial label. Moreover, the attacked models have more influence over the resulting adversarial label as compared to the adversarial attack algorithm itself. These finding are helpful in understanding the intriguing vulnerability of deep learning to adversarial examples.

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 3.147.126.122

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:
Jung, J.; Akhtar, N. and Hassan, G. (2021). Analysing Adversarial Examples for Deep Learning. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6; ISSN 2184-4321, SciTePress, pages 585-592. DOI: 10.5220/0010313705850592

@conference{visapp21,
author={Jason Jung. and Naveed Akhtar. and Ghulam Mubashar Hassan.},
title={Analysing Adversarial Examples for Deep Learning},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={585-592},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010313705850592},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Analysing Adversarial Examples for Deep Learning
SN - 978-989-758-488-6
IS - 2184-4321
AU - Jung, J.
AU - Akhtar, N.
AU - Hassan, G.
PY - 2021
SP - 585
EP - 592
DO - 10.5220/0010313705850592
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic