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Article
Automated Detection of Semagram-Laden Images Using Adaptive Neural Networks
Proceedings of SPIE
  • James D. Cannady, Jr., Nova Southeastern University
  • Paul S. Cerkez, Nova Southeastern University
Document Type
Article
Publication Date
4-23-2010
Abstract

Digital steganography has been used extensively for electronic copyright stamping, but also for criminal or covert activities. While a variety of techniques exist for detecting steganography the identification of semagrams, messages transmitted visually in a non-textual format remain elusive. The work that will be presented describes the creation of a novel application which uses hierarchical neural network architectures to detect the likely presence of a semagram message in an image. The application was used to detect semagrams containing Morse Code messages with over 80% accuracy. These preliminary results indicate a significant advance in the detection of complex semagram patterns.

DOI
10.1117/12.848474
Disciplines
Citation Information
James D. Cannady and Paul S. Cerkez. "Automated Detection of Semagram-Laden Images Using Adaptive Neural Networks" Proceedings of SPIE (2010) ISSN: 0277-786X
Available at: http://works.bepress.com/james-cannady/15/