"Steganography" is the art of communicating a secret message,
hiding the very existence of a secret message. This is typically
done by hiding the message within a non-sensitive document.
S"teganalysis" is the art and science of detecting such hidden
messages. The task in steganalysis is to take an object
(communication) and classify it as either a steganogram or a clean
document. Most recent solutions apply classification algorithms
from machine learning and pattern recognition, which tackle
problems too complex for analytical solution by teaching computers
to learn from empirical data.
Part 1of the book is an introduction to steganalysis as part of
the wider trend of multimedia forensics, as well as a practical
tutorial on machine learning in this context. Part 2 is a survey of
a wide range of feature vectors proposed for steganalysis with
performance tests and comparisons. Part 3 is an in-depth study of
machine learning techniques and classifier algorithms, and presents
a critical assessment of the experimental methodology and
applications in steganalysis.
Key features: Serves as a tutorial on the topic of steganalysis
with brief introductions to much of the basic theory provided, and
also presents a survey of the latest research.Develops and
formalises the application of machine learning in steganalysis;
with much of the understanding of machine learning to be gained
from this book adaptable for future study of machine learning in
other applications. Contains Python programs and algorithms to
allow the reader to modify and reproduce outcomes discussed in the
book.Includes companion software available from the author's
website.
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