Header logo is

Simple algorithmic modifications for improving blind steganalysis performance


Conference Paper


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.

Author(s): Schwamberger, V. and Franz, MO.
Journal: Proceedings of the 12th ACM workshop on Multimedia and Security (MM&Sec 2010)
Pages: 225-230
Year: 2010
Month: September
Day: 0
Editors: Campisi, P. , J. Dittmann, S. Craver
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1854229.1854268
Event Name: 12th ACM Workshop on Multimedia and Security (MM&Sec 2010)
Event Place: Roma, Italy

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Simple algorithmic modifications for improving blind steganalysis performance},
  author = {Schwamberger, V. and Franz, MO.},
  journal = {Proceedings of the 12th ACM workshop on Multimedia and Security (MM&Sec 2010)},
  pages = {225-230},
  editors = {Campisi, P. , J. Dittmann, S. Craver},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = sep,
  year = {2010},
  month_numeric = {9}