Modeling and Extending the Ensemble Classifier for Steganalysis of Digital Images Using Hypothesis Testing Theory

@article{Cogranne2015ModelingAE,
  title={Modeling and Extending the Ensemble Classifier for Steganalysis of Digital Images Using Hypothesis Testing Theory},
  author={R{\'e}mi Cogranne and Jessica J. Fridrich},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2015},
  volume={10},
  pages={2627-2642}
}
The machine learning paradigm currently predominantly used for steganalysis of digital images works on the principle of fusing the decisions of many weak base learners. In this paper, we employ a statistical model of such an ensemble and replace the majority voting rule with a likelihood ratio test. This allows us to train the ensemble to guarantee desired statistical properties, such as the false-alarm probability and the detection power, while preserving the high detection accuracy of… CONTINUE READING
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