Black-box Evolutionary Search for Adversarial Examples against Deep Image Classifiers in Non-Targeted Attacks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00534345" target="_blank" >RIV/67985807:_____/20:00534345 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/IJCNN48605.2020.9207688" target="_blank" >http://dx.doi.org/10.1109/IJCNN48605.2020.9207688</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN48605.2020.9207688" target="_blank" >10.1109/IJCNN48605.2020.9207688</a>
Alternative languages
Result language
angličtina
Original language name
Black-box Evolutionary Search for Adversarial Examples against Deep Image Classifiers in Non-Targeted Attacks
Original language description
Machine learning models exhibit vulnerability to adversarial examples i.e., artificially created inputs that become misinterpreted. The goal of this paper is to explore non-targeted black-box adversarial attacks on deep networks performing image classification. The original evolutionary algorithm for generating adversarial examples is proposed that employs a guided multi-objective search through the space of perturbed images. The efficiency of attacks is validated by experiments with the CIFAR-10 data set. The experimental results verify the usability of our approach against deep convolutional neural networks.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-23827S" target="_blank" >GA18-23827S: Capabilities and limitations of shallow and deep networks</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 International Joint Conference on Neural Networks (IJCNN): Conference Proceedings
ISBN
978-1-7281-6926-2
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
9207688
Publisher name
IEEE
Place of publication
Piscataway
Event location
Glasgow
Event date
Jul 19, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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