Detector Of Steganographyk Images With The Application Of Artifical Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10236830" target="_blank" >RIV/61989100:27240/17:10236830 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/17:10236830

Výsledek na webu

<a href="https://www.sgem.org/index.php/call-for-papers/conference-proceedings-sgem" target="_blank" >https://www.sgem.org/index.php/call-for-papers/conference-proceedings-sgem</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Detector Of Steganographyk Images With The Application Of Artifical Neural Network

Popis výsledku v původním jazyce

Introduction Steganography can be used for illegal activities. It is very important to be prepared. To detect steganography images we have counter-technique known as steganalysis. There are different types of steganalysis, depending on if the original artifact (cover) is known or not. In terms of practical use, most important are methods of “blind steganalysis”, that can be applied to image files and because we do not have the original cover for comparison. This article deals with the application of neural networks on the issues of steganalysis. The aim is to improve the detection capability of conventional steganalytical tools with using of artificial neural network and several improvements. In our work is important to understand the behavior of the targeted steganography algorithm. Then we can use it is weaknesses to increase the detection capability. In our case we are focus on steganography algorithm OutGuess2.0. We analyze the ability of the detector, which utilizes calibration process and blockiness calculation to detect the presence of steganography message in suspected image. We verify if the deployment of neural network improves this detection.

Název v anglickém jazyce

Detector Of Steganographyk Images With The Application Of Artifical Neural Network

Popis výsledku anglicky

Introduction Steganography can be used for illegal activities. It is very important to be prepared. To detect steganography images we have counter-technique known as steganalysis. There are different types of steganalysis, depending on if the original artifact (cover) is known or not. In terms of practical use, most important are methods of “blind steganalysis”, that can be applied to image files and because we do not have the original cover for comparison. This article deals with the application of neural networks on the issues of steganalysis. The aim is to improve the detection capability of conventional steganalytical tools with using of artificial neural network and several improvements. In our work is important to understand the behavior of the targeted steganography algorithm. Then we can use it is weaknesses to increase the detection capability. In our case we are focus on steganography algorithm OutGuess2.0. We analyze the ability of the detector, which utilizes calibration process and blockiness calculation to detect the presence of steganography message in suspected image. We verify if the deployment of neural network improves this detection.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/TF01000091" target="_blank" >TF01000091: Bezpečnost mobilních zařízení a komunikace</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

SGEM 2017 : proceedings of the 17th International Multidisciplinary Scientific GeoConference : June 29 – July 5, 2017, Albena, Bulgaria. Volume 17. Issue 54

ISBN

978-619-7408-11-9

ISSN

1314-2704

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

255-262

Název nakladatele

STEF92 Technology Ltd.

Místo vydání

Sofia

Místo konání akce

Albena

Datum konání akce

29. 6. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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