Vulnerability of classifiers to evolutionary generated adversarial examples

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00485639" target="_blank" >RIV/67985807:_____/20:00485639 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.neunet.2020.04.015" target="_blank" >http://dx.doi.org/10.1016/j.neunet.2020.04.015</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neunet.2020.04.015" target="_blank" >10.1016/j.neunet.2020.04.015</a>

Result language

angličtina

Original language name

Vulnerability of classifiers to evolutionary generated adversarial examples

Original language description

This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine learning model in the black-box attack scenario. This way, we can find adversarial examples without access to model’s parameters, only by querying the model at hand. We have tested a range of machine learning models including deep and shallow neural networks. Our experiments have shown that the vulnerability to adversarial examples is not only the problem of deep networks, but it spreads through various machine learning architectures. Rather, it depends on the type of computational units. Local units, such as Gaussian kernels, are less vulnerable to adversarial examples.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

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

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Networks

ISSN

0893-6080

e-ISSN

—

Volume of the periodical

127

Issue of the periodical within the volume

July

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

168-181

UT code for WoS article

000536453100016

EID of the result in the Scopus database

2-s2.0-85083895880