Beating White-Box Defenses with Black-Box Attacks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10437329" target="_blank" >RIV/00216208:11320/21:10437329 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/IJCNN52387.2021.9533772" target="_blank" >https://doi.org/10.1109/IJCNN52387.2021.9533772</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN52387.2021.9533772" target="_blank" >10.1109/IJCNN52387.2021.9533772</a>
Alternative languages
Result language
angličtina
Original language name
Beating White-Box Defenses with Black-Box Attacks
Original language description
Deep learning has achieved great results in the last decade, however, it is sensitive to so called adversarial attacks small perturbations of the input that cause the network to classify incorrectly. In the last years a number of attacks and defenses against these attacks were described. Most of the defenses however focus on defending against gradient-based attacks. In this paper, we describe an evolutionary attack and show that the adversarial examples produced by the attack have different features than those from gradient-based attacks. We also show that these features mean that one of the state-of-the-art defenses fails to detect such attacks.
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
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
ISBN
978-0-7381-3366-9
ISSN
2161-4393
e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
IEEE
Place of publication
NEW YORK
Event location
Online
Event date
Jul 18, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000722581703104