CCE Theses and Dissertations

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Date of Award


Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy in Computer Information Systems (DCIS)


Graduate School of Computer and Information Sciences


James Cannady

Committee Member

Sumitra Mukherjee

Committee Member

William Hafner


neural networks, semagram, steganography


Digital steganography is gaining wide acceptance in the world of electronic copyright stamping. Digital media that are easy to steal, such as graphics, photos and audio files, are being tagged with both visible and invisible copyright stamp known as a digital watermark. However, these same methodologies are also used to hide communications between actors in criminal or covert activities. An inherent difficulty in developing steganography attacks is overcoming the variety of methods for hiding a message and the multitude of choices of available media. The steganalyst cannot create an attack until the hidden content method appears. When a message is visually transmitted in a non-textual format (i.e., in an image) it is referred to as a semagram.

Semagrams are a subset of steganography and are relatively easy to create. However, detecting a hidden message in an image-based semagram is more difficult than detecting digital modifications to an image's structure. The trend in steganography is a decrease in detectable digital traces, and a move toward semagrams. This research outlines the creation of a novel, computer-based application, designed to detect the likely presence of a Morse Code based semagram message in an image. This application capitalizes on the adaptability and learning capabilities of various artificial neural network (NN) architectures, most notably hierarchical architectures.

Four NN architectures [feed-forward Back-Propagation NN (BPNN), Self organizing Map (SOM), Neural Abstraction Pyramid (NAP), and a Hybrid Custom Network (HCN)] were tested for applicability to this domain with the best performing one being the HCN. Each NN was given a baseline set of training images (quantity based on NN architecture) then test images were presented, (each test set having 3,337 images). There were 36 levels of testing. Each subsequent test set representing an increase in complexity over the previous one. In the end, the HCN proved to be the NN of choice from among the four tested. The final HCN implementation was the only network able to successfully perform against all 36 levels. Additionally, the HCN, while only being trained on the base Morse Code images, successfully detected images in the 9 test sets of Morse Code isomorphs.

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