The Challenges of Rich Features in Universal Steganalysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00210674" target="_blank" >RIV/68407700:21230/13:00210674 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1117/12.2006790" target="_blank" >http://dx.doi.org/10.1117/12.2006790</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2006790" target="_blank" >10.1117/12.2006790</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Challenges of Rich Features in Universal Steganalysis

Popis výsledku v původním jazyce

Contemporary steganalysis is driven by new steganographic rich feature sets, which consist of large numbers of weak features. Although extremely powerful when applied to supervised classification problems, they are not compatible with unsupervised universal steganalysis, because the unsupervised method cannot separate the signal (evidence of steganographic embedding) from the noise (cover content). This work tries to alleviate the problem, by means of feature extraction algorithms. We focus on linear projections informed by embedding methods, and propose a new method which we call calibrated least squares with the specific aim of making the projections sensitive to stego content yet insensitive to cover variation. Different projections are evaluated bytheir application to the anomaly detector from Ref. 1, and we are able to retain both the universality and the robustness of the method, while increasing its performance substantially.

Název v anglickém jazyce

The Challenges of Rich Features in Universal Steganalysis

Popis výsledku anglicky

Contemporary steganalysis is driven by new steganographic rich feature sets, which consist of large numbers of weak features. Although extremely powerful when applied to supervised classification problems, they are not compatible with unsupervised universal steganalysis, because the unsupervised method cannot separate the signal (evidence of steganographic embedding) from the noise (cover content). This work tries to alleviate the problem, by means of feature extraction algorithms. We focus on linear projections informed by embedding methods, and propose a new method which we call calibrated least squares with the specific aim of making the projections sensitive to stego content yet insensitive to cover variation. Different projections are evaluated bytheir application to the anomaly detector from Ref. 1, and we are able to retain both the universality and the robustness of the method, while increasing its performance substantially.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/GPP103%2F12%2FP514" target="_blank" >GPP103/12/P514: Detekce anomalií v reálném čase a časově nestálem prostředí</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Media Watermarking, Security, and Forensics 2013

ISBN

9780819494382

ISSN

—

e-ISSN

—

Počet stran výsledku

15

Strana od-do

—

Název nakladatele

SPIE

Místo vydání

Washington

Místo konání akce

San Francisco

Datum konání akce

3. 2. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000329576200020