When Should You Defend Your Classifier? A Game-Theoretical Analysis of Countermeasures Against Adversarial Examples
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00356237" target="_blank" >RIV/68407700:21230/21:00356237 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-90370-1_9" target="_blank" >https://doi.org/10.1007/978-3-030-90370-1_9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-90370-1_9" target="_blank" >10.1007/978-3-030-90370-1_9</a>
Alternative languages
Result language
angličtina
Original language name
When Should You Defend Your Classifier? A Game-Theoretical Analysis of Countermeasures Against Adversarial Examples
Original language description
Adversarial machine learning, i.e., increasing the robustness of machine learning algorithms against so-called adversarial examples, is now an established field. Yet, newly proposed methods are evaluated and compared under unrealistic scenarios where costs for adversary and defender are not considered and either all samples or no samples are adversarially perturbed. We scrutinize these assumptions and propose the advanced adversarial classification game, which incorporates all relevant parameters of an adversary and a defender. Especially, we take into account economic factors on both sides and the fact that all so far proposed countermeasures against adversarial examples reduce accuracy on benign samples. Analyzing the scenario in detail, where both players have two pure strategies, we identify all best responses and conclude that in practical settings, the most influential factor might be the maximum amount of adversarial examples.
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
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
International Conference on Decision and Game Theory for Security
ISBN
978-3-030-90369-5
ISSN
0302-9743
e-ISSN
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Number of pages
20
Pages from-to
158-177
Publisher name
Springer Nature Switzerland AG
Place of publication
Basel
Event location
Online conference
Event date
Oct 25, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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