Steganalysis by means of Artificial Neural Networks: Steganography detection in JPEG files by means of Artifical Neural Networks using Huffman coding

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F12%3A43868933" target="_blank" >RIV/70883521:28140/12:43868933 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Steganalysis by means of Artificial Neural Networks: Steganography detection in JPEG files by means of Artifical Neural Networks using Huffman coding

Popis výsledku v původním jazyce

This book is focused on the revealing of hidden information present in multimedia files, mainly in pictures. This hidden information (messages) is coded in by means of steganography, which is an additional method of cryptography. Steganography provides better security for messages and the detection of such a message is not easy. The main goal of this research is a classification by means of artificial neural networks aimed at reducing false positive classification results to a minimum. To accomplish themain goal, a new model of image pre-processing was proposed. This pre-processing model is based on the Huffman coding, which is the main part of the lossless compression algorithm used in JPEG images. The Huffman coding can be easily transformed into the training sets for the artificial neural network, which is used as a classifier. The type of used artificial neural network was feed forward with supervision and Levenberg-Marquardt training algorithm. The results from performed simulati

Název v anglickém jazyce

Steganalysis by means of Artificial Neural Networks: Steganography detection in JPEG files by means of Artifical Neural Networks using Huffman coding

Popis výsledku anglicky

This book is focused on the revealing of hidden information present in multimedia files, mainly in pictures. This hidden information (messages) is coded in by means of steganography, which is an additional method of cryptography. Steganography provides better security for messages and the detection of such a message is not easy. The main goal of this research is a classification by means of artificial neural networks aimed at reducing false positive classification results to a minimum. To accomplish themain goal, a new model of image pre-processing was proposed. This pre-processing model is based on the Huffman coding, which is the main part of the lossless compression algorithm used in JPEG images. The Huffman coding can be easily transformed into the training sets for the artificial neural network, which is used as a classifier. The type of used artificial neural network was feed forward with supervision and Levenberg-Marquardt training algorithm. The results from performed simulati

Druh

B - Odborná kniha

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/ED2.1.00%2F03.0089" target="_blank" >ED2.1.00/03.0089: Centrum bezpečnostních, informačních a pokročilých technologií (CEBIA-Tech)</a><br>

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

ISBN

978-3-659-30172-8

Počet stran knihy

132

Název nakladatele

Lap Lambert Academic Publishing

Místo vydání

Saarbrücken

Kód UT WoS knihy

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