Steganalysis by subtractive pixel adjacency matrix

  • Authors:
  • Tomáš Pevný;Patrick Bas;Jessica Fridrich

  • Affiliations:
  • Czech Technical University in Prague, FEE, Department of Cybernetics, Agent Technology Center, Prague, Czech Republic;Gipsa-Lab, INPG-Gipsa-Lab, Grenoble, France and Lagis, Ecole Centrale de Lille, Lille, France;Department of Electrical and Computer Engineering, Binghamton University, NY

  • Venue:
  • IEEE Transactions on Information Forensics and Security
  • Year:
  • 2010

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Abstract

This paper presents a method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is least significant bit (LSB) matching. First, arguments are provided for modeling the differences between adjacent pixels using firstorder and second-order Markov chains. Subsets of sample transition probability matrices are then used as features for a steganalyzer implemented by support vector machines. The major part of experiments, performed on four diverse image databases, focuses on evaluation of detection of LSB matching. The comparison to prior art reveals that the presented feature set offers superior accuracy in detecting LSB matching. Even though the feature set was developed specifically for spatial domain steganalysis, by constructing steganalyzers for ten algorithms for JPEG images, it is demonstrated that the features detect steganography in the transform domain as well.