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Proceedings Paper

Improvement of information fusion-based audio steganalysis
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Paper Abstract

In the paper we extend an existing information fusion based audio steganalysis approach by three different kinds of evaluations: The first evaluation addresses the so far neglected evaluations on sensor level fusion. Our results show that this fusion removes content dependability while being capable of achieving similar classification rates (especially for the considered global features) if compared to single classifiers on the three exemplarily tested audio data hiding algorithms. The second evaluation enhances the observations on fusion from considering only segmental features to combinations of segmental and global features, with the result of a reduction of the required computational complexity for testing by about two magnitudes while maintaining the same degree of accuracy. The third evaluation tries to build a basis for estimating the plausibility of the introduced steganalysis approach by measuring the sensibility of the models used in supervised classification of steganographic material against typical signal modification operations like de-noising or 128kBit/s MP3 encoding. Our results show that for some of the tested classifiers the probability of false alarms rises dramatically after such modifications.

Paper Details

Date Published: 27 January 2010
PDF: 11 pages
Proc. SPIE 7542, Multimedia on Mobile Devices 2010, 75420A (27 January 2010); doi: 10.1117/12.838869
Show Author Affiliations
Christian Kraetzer, Otto-von-Guericke-Univ. Magdeburg (Germany)
Jana Dittmann, Otto-von-Guericke-Univ. Magdeburg (Germany)

Published in SPIE Proceedings Vol. 7542:
Multimedia on Mobile Devices 2010
Reiner Creutzburg; David Akopian, Editor(s)

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