Blind steganalysis using fractal features

Date of Award


Degree Type


Degree Name

Doctor of Philosophy


School of Science

First Advisor

Professor Craig Valli

Second Advisor

Associate Professor Mike Johnstone


A novel approach for detecting Steganographic images with blind steganalysis using fractal
features has been proposed in this thesis. Two overarching methods were used to construct
the feature vector; first, using a variation of the Differential Box Counting algorithm for
lacunarity estimation to extract the fractal features; and then, using dynamic time warping
for similarity measures as the basis for further deriving other features.
The research design enabled the proposition of four major approaches that were based on
iterative experiments that aided in further improving and extending upon the previous
This research has thus made three major contributions to the body of knowledge by the
1. Proposing of a novel approach for constructing the feature vector based on fractal
features for blind steganalysis.
2. Ability to perform significant feature reduction by using the proposed fractal fea-
tures, which is also applicable in areas other than steganalysis.
3. Discovery of an improved blind steganalysis approach for known Cover images.

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