Blind steganalysis using fractal features
Date of Award
8-23-2016
Document Type
Thesis
Publisher
Edith Cowan University
Degree Name
Doctor of Philosophy
School
School of Science
First Supervisor
Professor Craig Valli
Second Supervisor
Associate Professor Mike Johnstone
Abstract
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
outcomes.
This research has thus made three major contributions to the body of knowledge by the
following:
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.
Access Note
Access to this thesis is not available.
Recommended Citation
Ibrahim, A. (2016). Blind steganalysis using fractal features. Edith Cowan University. Retrieved from https://ro.ecu.edu.au/theses/2198