Evolving decision tree rule based system for audio stego anomalies detection based on Hausdorff distance statistics

  • Authors:

  • S. Geetha;N. Ishwarya;N. Kamaraj

  • Affiliations:

  • Network Security Labs in Department of Information Technology, Thiagarajar College of Engineering, India;Network Security Labs in Department of Information Technology, Thiagarajar College of Engineering, India;Electrical and Electronics Engineering Department, Thiagarajar College of Engineering, India

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

This work is motivated by the interest in forensics steganalysis which is aimed at detecting the presence of secret messages transmitted through a subliminal channel. A critical part of the steganalyser design depends on the choice of stego-sensitive features and an efficient machine learning paradigm. The goals of this paper are: (1) to demonstrate that the higher-order statistics of Hausdorff distance - a dissimilarity metric, offers potential discrimination ability for a clean and a stego audio and (2) to achieve promising classification accuracy by realizing the proposed steganalyser with evolving decision tree classifier. Stego sensitive feature selection process is imparted by the genetic algorithm (GA) component and the construction of the rule base is facilitated by the decision tree module. The objective function is designed to maximize the Precision and Recall measures of the classifier thereby enhancing the detection accuracy of the system with low-dimensional and informative features. An extensive experimental evaluation of the proposed system on a database containing 4800 clean and stego audio files (generated by using six different embedding schemes), with the family of six GA decision trees was conducted. The observations reported as 90%+ detection rate, a promising score for a blind steganalyser, show that the proposed scheme, with the Hausdorff distance statistics as features and the evolving decision tree as classifier, is a state-of-the-art steganalyser that outperforms many of the previous steganalytic methods.