Multi-class classification fusion using boosting for identifying steganography methods

Benjamin M. Rodriguez; Gilbert L. Peterson

doi:10.1117/12.777328

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17 March 2008 Multi-class classification fusion using boosting for identifying steganography methods

Benjamin M. Rodriguez, Gilbert L. Peterson

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Proceedings Volume 6974, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008; 697407 (2008) https://doi.org/10.1117/12.777328
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

There are over 250 image steganography methods available on the Internet. In digital image steganalysis an analyst has three goals, first determine if an embedded message exists, next determine the embedding method used to create the stego image and finally extract the hidden message. The objective of this paper lies on the second goal, that is, to identify the embedding technique used to create the steganography image. Several detection systems currently exist, so the identification problem becomes one of determining which detection system has correctly identified the embedding method. In this work, the individual detection systems are fused using boosting. Boosting is a powerful technique for combining an ensemble of base classifiers to produce a form of committee with improved performance over any of the single classifiers in the ensemble. The results in this paper show that boosting takes advantage of the individual strengths from each detection systems and classification performance is increased by 10%.

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Benjamin M. Rodriguez and Gilbert L. Peterson "Multi-class classification fusion using boosting for identifying steganography methods", Proc. SPIE 6974, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008, 697407 (17 March 2008); https://doi.org/10.1117/12.777328

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