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Simple algorithmic modifications for improving blind steganalysis performance

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84203

Schwamberger,  V
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83919

Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Schwamberger, V., & Franz, M. (2010). Simple algorithmic modifications for improving blind steganalysis performance. In 12th ACM Workshop on Multimedia and Security (MM&Sec 2010) (pp. 225-230). New York, NY, USA: ACM Press.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BE72-1
Zusammenfassung
Most current algorithms for blind steganalysis of images are based on a two-stages approach: First, features are extracted in order to reduce dimensionality and to highlight potential manipulations; second, a classifier trained on pairs of clean and stego images finds a decision rule for these features to detect stego images. Thereby, vector components might vary significantly in their values, hence normalization of the feature vectors is crucial. Furthermore, most classifiers contain free parameters, and an automatic model selection step has to be carried out for adapting these parameters. However, the commonly used cross-validation destroys some information needed by the classifier because of the arbitrary splitting of image pairs (stego and clean version) in the training set. In this paper, we propose simple modifications of normalization and for standard cross-validation. In our experiments, we show that these methods lead to a significant improvement of the standard blind steganalyzer of Lyu and Farid.