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Blind Image Steganalytic Optimization by using Machine Learning
Halmstad University, School of Information Technology.
2018 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Since antiquity, steganography has existed in protecting sensitive information against unauthorized unveiling attempts. Nevertheless, digital media’s evolution, reveals that steganography has been used as a tool for activities such as terrorism or child pornography. Given this background, steganalysis arises as an antidote to steganography. Steganalysis can be divided into two main approaches: universal – also called blind – and specific. Specific methods request a previous knowledge of the steganographic technique under analysis. On the other hand, universal methods which can be widely practiced in a variety of algorithms, are more adaptable to real-world applications. Thus, it is necessary to establish even more accurate steganalysis techniques

capable of detecting the hidden information coming from the use of diverse steganographic methods. Considering this, a universal steganalysis method specialized in images is proposed. The method is based on the typical steganalysis process, where feature extractors and classifiers are used. The experiments were implemented on different embedding rates and for various steganographic techniques. It turns out that the proposed method succeeds for the most part, providing dignified results on color images and promising results on gray-scale images.

Place, publisher, year, edition, pages
2018.
Keywords [en]
steganography, steganalysis, machine learning, stelanology
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-38150OAI: oai:DiVA.org:hh-38150DiVA, id: diva2:1255395
Subject / course
Digital Forensics
Educational program
Master's Programme in Network Forensics, 60 credits
Supervisors
Examiners
Available from: 2018-11-13 Created: 2018-10-12 Last updated: 2018-11-13Bibliographically approved

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Electrical Engineering, Electronic Engineering, Information Engineering

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf