Is Ensemble Classifier Needed for Steganalysis in High-Dimensional Feature Spaces?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00237781" target="_blank" >RIV/68407700:21230/15:00237781 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/WIFS.2015.7368597" target="_blank" >http://dx.doi.org/10.1109/WIFS.2015.7368597</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WIFS.2015.7368597" target="_blank" >10.1109/WIFS.2015.7368597</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Is Ensemble Classifier Needed for Steganalysis in High-Dimensional Feature Spaces?

Popis výsledku v původním jazyce

The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble. This paper challenges this assumption by showing that linear classifier obtained by various regularizations of the FLD can perform equally well as the ensemble. Moreover it demonstrates that using state of the art solvers linear classifiers can be trained more efficiently and offer certain potential advantages over the original ensemble leading to much lower computational complexity than the ensemble classifier. All claims are supported experimentally

Název v anglickém jazyce

Is Ensemble Classifier Needed for Steganalysis in High-Dimensional Feature Spaces?

Popis výsledku anglicky

The ensemble classifier, based on Fisher Linear Discriminant base learners, was introduced specifically for steganalysis of digital media, which currently uses high-dimensional feature spaces. Presently it is probably the most used method to design supervised classifier for steganalysis of digital images because of its good detection accuracy and small computational cost. It has been assumed by the community that the classifier implements a non-linear boundary through pooling binary decision of individual classifiers within the ensemble. This paper challenges this assumption by showing that linear classifier obtained by various regularizations of the FLD can perform equally well as the ensemble. Moreover it demonstrates that using state of the art solvers linear classifiers can be trained more efficiently and offer certain potential advantages over the original ensemble leading to much lower computational complexity than the ensemble classifier. All claims are supported experimentally

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 7th International Workshop on Forensics and Security

ISBN

978-1-4673-6802-5

ISSN

—

e-ISSN

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Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

IEEE Signal Processing Society

Místo vydání

New Jersey

Místo konání akce

Rome

Datum konání akce

16. 11. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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