Improving hidden message extraction using LSB steganalysis techniques

Nikhil Mewalal, Wai Sze Leung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Increased awareness of the role of digital forensics in investigations has led to greater efforts being employed by users to conceal their data, possibly even using algorithms purposely designed to evade detection during steganalysis. A digital investigator seeking to ascertain whether some medium is indeed making use of steganography to hide pertinent evidence must therefore consider including other steganalysis techniques in their analysis in order to overcome the different steganographic strategies that may be used to evade detection. This paper investigates the design of a more comprehensive steganalysis tool that makes use of a series of statistical methods in conjunction with visual and forensic methods to detect messages hidden in images, specifically those hidden in PNG files using Least Significant Bit steganography. The study devises an appropriate combination of the techniques to generate a more effective and comprehensive steganalysis strategy for digital investigators attempting to detect hidden data.

Original languageEnglish
Title of host publicationInformation Science and Applications 2018 - ICISA 2018
EditorsKuinam J. Kim, Kuinam J. Kim, Nakhoon Baek
PublisherSpringer Verlag
Pages273-284
Number of pages12
ISBN (Print)9789811310553
DOIs
Publication statusPublished - 2019
EventInternational Conference on Information Science and Applications, ICISA 2018 - Kowloon, Hong Kong
Duration: 25 Jun 201827 Jun 2018

Publication series

NameLecture Notes in Electrical Engineering
Volume514
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Information Science and Applications, ICISA 2018
Country/TerritoryHong Kong
CityKowloon
Period25/06/1827/06/18

Keywords

  • Least significant bit
  • Steganalysis
  • Steganography

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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