Automated detection of semagram-laden images using adaptive neural networks

Paul S. Cerkez; James D. Cannady

doi:10.1117/12.848474

28 April 2010 Automated detection of semagram-laden images using adaptive neural networks

Paul S. Cerkez, James D. Cannady

Proceedings Volume 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010; 77080M (2010) https://doi.org/10.1117/12.848474
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

Paul S. Cerkez and James D. Cannady "Automated detection of semagram-laden images using adaptive neural networks", Proc. SPIE 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010, 77080M (28 April 2010); https://doi.org/10.1117/12.848474

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available