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Steganography

  • Steganographic Algorithms
  • Syndrome-Trellis Codes Toolbox
  • Steganography Design
  • Gibbs Construction in Steganography
  • Simulator of nsF5
  • Perturbed Quantization

Steganalysis

  • Feature Extractors
  • Are we there yet?
  • Ensemble Classifier
  • Low-complexiy Linear Classifier
  • LRT Linear Classifier
  • Histogram Layer
  • Explicit Feature Maps
  • SPAM features
  • Extractor of 274/548 Merged Features
  • Structural LSB Detectors
  • Quantitative Steganalysis Using Rich Models

Digital Forensics

  • Camera Fingerprint

Image Database

  • BOSSbase 1.01

Quantitative steganalysis using rich models

Description

Matlab implementation of the framework for quantitative steganalysis in high-dimensional feature spaces as proposed in [1]. The algorithm is based on gradient boosting [2] and utilizes different random subspace at each stage.

The main function, GBM_steganalysis.m is accompanied by an example script example.m.

Paper abstract

In this paper, we propose a regression framework for steganalysis of digital images that utilizes the recently proposed rich models -- high-dimensional statistical image descriptors that have been shown to substantially improve classical (binary) steganalysis. Our proposed system is based on gradient boosting and utilizes a steganalysis-specific variant of regression trees as base learners. The conducted experiments confirm that the proposed system outperforms prior quantitative steganalysis (both structural and feature-based) across a wide range of steganographic schemes: HUGO, LSB replacement, nsF5, BCHopt, and MME3.

Contact

  • Jan Kodovský - jan (dot) kodovsky (at) binghamton (dot) edu
  • Jessica Fridrich - fridrich (at) binghamton (dot) edu

Download

  • GBM_steganalysis.zip (19 MB, includes also sample feature files)

References

[1] J. Kodovský and J. Fridrich, Quantitative Steganalysis Using Rich Models, SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics XV, San Francisco, CA, February 3-7, 2013.

[2] J. H. Friedman, Greedy function approximation: A gradient boosting machine, Annals of Statistics, 29:1189–1232, 2000.


Last update: January 2013