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Steganalysis of Content-Adaptive Steganography in Spatial Domain

  • Jessica Fridrich
  • Jan Kodovský
  • Vojtěch Holub
  • Miroslav Goljan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6958)

Abstract

Content-adaptive steganography constrains its embedding changes to those parts of covers that are difficult to model, such as textured or noisy regions. When combined with advanced coding techniques, adaptive steganographic methods can embed rather large payloads with low statistical detectability at least when measured using feature-based steganalyzers trained on a given cover source. The recently proposed steganographic algorithm HUGO is an example of this approach. The goal of this paper is to subject this newly proposed algorithm to analysis, identify features capable of detecting payload embedded using such schemes and obtain a better picture regarding the benefit of adaptive steganography with public selection channels. This work describes the technical details of our attack on HUGO as part of the BOSS challenge.

Keywords

Cover Image Neighboring Pixel Mean Absolute Deviation Stego Image Fisher Linear Discriminant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jessica Fridrich
    • 1
  • Jan Kodovský
    • 1
  • Vojtěch Holub
    • 1
  • Miroslav Goljan
    • 1
  1. 1.Department of ECESUNY BinghamtonUSA

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