Steganalysis based on differential statistics

  • Authors:

  • Zugen Liu;Lingdi Ping;Jian Chen;Jimin Wang;Xuezeng Pan

  • Affiliations:

  • College of Computer Science, Zhejiang University, Hangzhou, China;College of Computer Science, Zhejiang University, Hangzhou, China;College of Computer Science, Zhejiang University, Hangzhou, China;College of Computer Science, Zhejiang University, Hangzhou, China;College of Computer Science, Zhejiang University, Hangzhou, China

  • Venue:
  • CANS'06 Proceedings of the 5th international conference on Cryptology and Network Security
  • Year:
  • 2006

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Abstract

Differential statistics were proposed in this paper to disclose the existence of hidden data in grayscale raw images. Meanwhile, differential statistics were utilized to improve the algorithm introduced by Fridrich to attack steganographic schemes in grayscale JPEG images. In raw images, to describe the correlation between data and their spatial positions, co-occurrence matrix based on intensities of adjacent pixels was adopted and the use of co-occurrence matrix was extended to high-order differentiations. The COMs (center of mass) of HCFs (histogram character function) were calculated from these statistics to form a 30-dimensional feature vector for steganalysis. For JPEG files, differential statistics were collected from boundaries of DCT blocks in their decompressed images. The COM of HCF was computed for each of these differential statistics and statistics from DCT domain so that a 28-dimensional feature vector can be extracted from a JPEG image. Two blindly steganalytic algorithms were constructed based on Support Vector Machine and the two kinds of feature vectors respectively. The presented methods demonstrate higher detecting rates with lower false positives than known schemes.