Rémi Cogranne, Tomas Denemark, Jessica Fridrich,

Theoretical Model of the FLD Ensemble Classifier Based on Hypothesis Testing Theory

Proc. of IEEE International Workshop on Information Forensics and Security(WIFS)

1–5

IEEE

2014

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

• LM2S
• Cyber-sécurité

• RC_WIFS_2014_ECSP.pdf (0.3 Mo)