LSB steganalysis using support vector regression

Erwei Lin, Edward Woertz, Moshe Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe a method of detecting the existence of messages, which are randomly scattered in the least significant bits (LSB) of both 24-bit RGB color and 8-bit grayscale images. The method is based on gathering and inspecting a set of image relevant features from the pixel groups of the stego-image, whose similarities and correlations change with different ratios of LSB embedding. The proposed detection scheme is based on Support Vector Regression (SVR). It is shown that the measurement of a selected set of features forms a multidimensional feature space which allows estimation of the length of hidden messages embedded in the LSB of cover-images with high precision.

Original languageEnglish (US)
Title of host publicationProceedings fron the Fifth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC
Pages95-100
Number of pages6
StatePublished - 2004
Externally publishedYes
EventProceedings fron the Fifth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC - West Point, NY, United States
Duration: Jun 10 2004Jun 11 2004

Publication series

NameProceedings fron the Fifth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC

Other

OtherProceedings fron the Fifth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC
CountryUnited States
CityWest Point, NY
Period6/10/046/11/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Information detection
  • SVM
  • Steganalysis

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