On the Extraction of Spread-Spectrum Hidden Data in Digital Media

Abstract

This paper considers the problem of blindly extracting data embedded over a wide band in a spectrum (transform) domain of a digital medium (image, audio, video). We first develop a multi-signature iterative generalized least-squares (MIGLS) core procedure to seek unknown data hidden in hosts via multi-signature direct-sequence spread-spectrum embedding. Neither the original host nor the embedding signatures are assumed available. Then, cross-correlation enhanced M-IGLS (CCM-IGLS), a procedure described herein in detail that is based on statistical analysis of repeated independent M-IGLS processing of the host, is seen to offer most effective hidden message recovery. Experimental studies on images show that the proposed CC-MIGLS algorithm can achieve recovery probability of error close to what may be attained with known embedding signatures and host autocorrelation matrix.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2012
Accession Number
ADA562747

Entities

People

  • Dimitris A. Pados
  • John D. Matyjas
  • Michael J. Medley
  • Michel Kulhandjian
  • Ming Li
  • Stella N. Batalama

Organizations

  • University at Buffalo

Tags

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Autocorrelation
  • Code Division Multiple Access
  • Communication Systems
  • Cross Correlation
  • Data Mining
  • Data Science
  • Digital Data
  • Digital Media
  • Electrical Engineering
  • Embedding
  • Information Science
  • Information Systems
  • Probability
  • Spread Spectrum
  • Statistical Analysis

Readers

  • Approximation Theory.
  • Radio communications and signal processing.