Abstract
The development of steganography techniques does not occur in isolation. There is an
arms race between steganography and steganalysis techniques. The development of a steganography
technique that could adapt when needed could be beneficial.
This research begins with a literature study exploring existing methods. Both steganog- raphy and
steganalysis approaches are covered in order to get an overview of the environ- ment. Optimization
methods are also examined to find a suitable method of optimizing the developed algorithm.
The information that is gathered in the literature study was then used to develop a
steganography algorithm that aims to decrease detectability through the strategic placement of
information. The algorithm is developed in such a way as to allow for optimization. A genetic
algorithm is implemented to help optimize the embedding of the information in a specific
environment. This should allow the algorithm to be re- optimized when new steganalysis techniques
are developed. The algorithm should thus remain relevant as steganalysis advances.
The developed algorithms show that the placement of information in the image has an effect on its
detectability. The developed algorithm even outperforms the random distribution LSB technique. The
optimization that was implemented also produced positive results.
The dissertation also includes the development of a novel evolutionary algorithm that drew its
inspiration from the aphid life cycle. By switching between reproduction strate- gies the algorithm
is able to adjust the balance between exploration and exploitation.
M.Sc. (Computer Science)