Identifying a steganographer in realistic and heterogeneous data sets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00200173" target="_blank" >RIV/68407700:21230/12:00200173 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1117/12.910565" target="_blank" >http://dx.doi.org/10.1117/12.910565</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.910565" target="_blank" >10.1117/12.910565</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identifying a steganographer in realistic and heterogeneous data sets

Popis výsledku v původním jazyce

We consider the problem of universal pooled steganalysis, in which we aim to identify a steganographer who sends many images (some of them innocent) in a network of many other innocent users. The detector must deal with multiple users and multiple imagesper user, and particularly the differences between cover sources used by different users. Despite being posed for five years, this problem has only previously been addressed by our 2011 paper. We extend our prior work in two ways. First, we present experiments in a new, highly realistic, domain: up to 4000 actors each transmitting up to 200 images, real-world data downloaded from a social networking site. Second, we replace hierarchical clustering by the method called local outlier factor (LOF), givinggreater accuracy of detection, and allowing a guilty actor sending moderate payloads to be detected, even amongst thousands of other actors sending hundreds of thousands of images.

Název v anglickém jazyce

Identifying a steganographer in realistic and heterogeneous data sets

Popis výsledku anglicky

We consider the problem of universal pooled steganalysis, in which we aim to identify a steganographer who sends many images (some of them innocent) in a network of many other innocent users. The detector must deal with multiple users and multiple imagesper user, and particularly the differences between cover sources used by different users. Despite being posed for five years, this problem has only previously been addressed by our 2011 paper. We extend our prior work in two ways. First, we present experiments in a new, highly realistic, domain: up to 4000 actors each transmitting up to 200 images, real-world data downloaded from a social networking site. Second, we replace hierarchical clustering by the method called local outlier factor (LOF), givinggreater accuracy of detection, and allowing a guilty actor sending moderate payloads to be detected, even amongst thousands of other actors sending hundreds of thousands of images.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of SPIE, Volume 8303

ISBN

9780819489500

ISSN

0277-786X

e-ISSN

—

Počet stran výsledku

13

Strana od-do

—

Název nakladatele

Society of Photo-optical Instrumentation Engineers, SPIE - International Society for Optical Engineering

Místo vydání

Pennsylvania State University. University Park,

Místo konání akce

San Francisco

Datum konání akce

22. 1. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000301416400017