Authors
Title
Theoretical Model of the FLD Ensemble Classifier Based on Hypothesis Testing Theory
In
Proc. of IEEE International Workshop on Information Forensics and Security(WIFS)
Pages
1–5
Publisher
IEEE
Year
2014
Indexed by


Abstract
The FLD ensemble classifier is a widely used ma-
chine learning tool for steganalysis of digital media due to its
efficiency when working with high dimensional feature sets. This
paper explains how this classifier can be formulated within the
framework of optimal detection by using an accurate statistical
model of base learners’ projections and the hypothesis testing
theory. A substantial advantage of this formulation is the ability
to theoretically establish the test properties, including the proba-
bility of false alarm and the test power, and the flexibility to use
other criteria of optimality than the conventional total probability
of error. Numerical results on real images show the sharpness
of the theoretically established results and the relevance of the
proposed methodology
Affiliations
Offprint
- RC_WIFS_2014_ECSP.pdf (0.3 Mo)