October 2012
Intermediate to advanced
304 pages
8h 22m
English
Content preview from Machine Learning in Image SteganalysisBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 9
Calibration Techniques
One of the key challenges in steganalysis is that most features vary a lot within each class, and sometimes more than between classes. What if we could calibrate the features by estimating what the feature would have been for the cover image?
Several such calibration techniques have been proposed in the literature. We will discuss two of the most well-known ones, namely the JPEG calibration of Fridrich et al. (2002) and calibration by downsampling as introduced by Ker (2005b). Both of these techniques aim to estimate the features of the cover image. In Section 9.4, we will discuss a generalisation of calibration, looking beyond cover estimates.
9.1 Calibrated Features
We will start by considering calibration techniques aiming to estimate the features of the cover image, and introduce key terminology and notation.
We view a feature vector as a function 
, where 
is the image space (e.g. 
for 8-bit grey-scale images). A reference transform is any function 
. Given an image , the transformed image is called the reference image.
If, for any cover image and ...
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