Deep Networks with RBF Layers to Prevent Adversarial Examples

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00490841" target="_blank" >RIV/67985807:_____/18:00490841 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-319-91253-0_25" target="_blank" >http://dx.doi.org/10.1007/978-3-319-91253-0_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-91253-0_25" target="_blank" >10.1007/978-3-319-91253-0_25</a>

Result language

angličtina

Original language name

Deep Networks with RBF Layers to Prevent Adversarial Examples

Original language description

We propose a simple way to increase the robustness of deep neural network models to adversarial examples. The new architecture obtained by stacking deep neural network and RBF network is proposed. It is shown on experiments that such architecture is much more robust to adversarial examples than the original one while its accuracy on legitimate data stays more or less the same.

Czech name

—

Czech description

—

Type

D - Article in proceedings

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

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

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

Article name in the collection

Artificial Intelligence and Soft Computing

ISBN

978-3-319-91252-3

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

10

Pages from-to

257-266

Publisher name

Springer

Place of publication

Cham

Event location

Zakopane

Event date

Jun 3, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000552718500025