<div class="csl-bib-body">
<div class="csl-entry">Marchisio, A., Caramia, G., Martina, M., & Shafique, M. (2022). fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems. In <i>Proceedings 2022 International Joint Conference on Neural Networks (IJCNN)</i> (pp. 1–9). https://doi.org/10.1109/IJCNN55064.2022.9892612</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/142190
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dc.description.abstract
Recently, Deep Neural Networks (DNNs) have achieved remarkable performances in many applications, while several studies have enhanced their vulnerabilities to malicious attacks. In this paper, we emulate the effects of natural weather conditions to introduce plausible perturbations that mislead the DNNs. By observing the effects of such atmospheric perturbations on the camera lenses, we model the patterns to create different masks that fake the effects of rain, snow, and hail. Even though the perturbations introduced by our attacks are visible, their presence remains unnoticed due to their association with natural events, which can be especially catastrophic for fully-autonomous and unmanned vehicles. We test our proposed fake Weather attacks on multiple Convolutional Neural Network and Capsule Network models, and report noticeable accuracy drops in the presence of such adversarial perturbations. Our work introduces a new security threat for DNNs, which is especially severe for safety-critical applications and autonomous systems.
en
dc.language.iso
en
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dc.subject
Adversarial Attacks
en
dc.subject
Deep Neural Networks
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dc.subject
Hail
en
dc.subject
Rain
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dc.subject
Snow
en
dc.subject
Weather
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dc.title
fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
Politecnico di Torino
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dc.contributor.affiliation
Politecnico di Torino
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dc.contributor.affiliation
New York Univeersity Abu Dhabi (NYUAD), United Arab Emirates
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dc.relation.isbn
978-1-7281-8671-9
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dc.description.startpage
1
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dc.description.endpage
9
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings 2022 International Joint Conference on Neural Networks (IJCNN)
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tuw.container.volume
2022-July
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tuw.peerreviewed
true
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tuw.researchTopic.id
I2
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tuw.researchTopic.name
Computer Engineering and Software-Intensive Systems
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E191-02 - Forschungsbereich Embedded Computing Systems
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tuw.publisher.doi
10.1109/IJCNN55064.2022.9892612
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dc.description.numberOfPages
9
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tuw.author.orcid
0000-0002-0689-4776
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tuw.author.orcid
0000-0002-3069-0319
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tuw.event.name
2022 International Joint Conference on Neural Networks (IJCNN)
en
tuw.event.startdate
18-07-2022
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tuw.event.enddate
23-07-2022
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tuw.event.online
Hybrid
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tuw.event.type
Event for scientific audience
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tuw.event.place
Padua
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tuw.event.country
IT
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tuw.event.presenter
Marchisio, Alberto
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.value
100
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item.openairetype
Inproceedings
-
item.openairetype
Konferenzbeitrag
-
item.grantfulltext
restricted
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item.cerifentitytype
Publications
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item.cerifentitytype
Publications
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item.languageiso639-1
en
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item.openairecristype
http://purl.org/coar/resource_type/c_18cf
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item.openairecristype
http://purl.org/coar/resource_type/c_18cf
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item.fulltext
no Fulltext
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crisitem.author.dept
E191-02 - Forschungsbereich Embedded Computing Systems
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crisitem.author.dept
Politecnico di Torino
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crisitem.author.dept
Politecnico di Torino, Turin, Italy
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crisitem.author.dept
New York Univeersity Abu Dhabi (NYUAD), United Arab Emirates