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Detecting hidden messages using higher-order statistics and support vectorachines

  • Dartmouth College

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

287 Scopus citations

Abstract

Techniques for information hiding have become increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting hidden messages has become considerably more difficult. This paper describes an approach to detecting hidden messages in images that uses a wavelet-like decomposition to build higher-order statistical models of natural images. Support vector machines are then used to discriminate between untouched and adulterated images.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsFabien A. P. Petitcolas
PublisherSpringer Verlag
Pages340-354
Number of pages15
ISBN (Print)3540004211
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2578
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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