19 July 2013 Multi-class Markov models for JPEG steganalysis
Author Affiliations +
Proceedings Volume 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013); 887847 (2013) https://doi.org/10.1117/12.2031619
Event: Fifth International Conference on Digital Image Processing, 2013, Beijing, China
Abstract
Partially ordered Markov models based features were proposed in very recent years, which were shown to be quite effective in JPEG steganalysis. This paper presents an improvement of the original models. The proposed models here have two new characters. First, they are established on absolute values of coefficients instead of the values themselves. Second, the Markov models for coefficients were classified by comparing JPEG modes, not by directions. Besides, we recommended using Cartesian calibration technique to enhance the corresponding steganalytic features. Experimental results show that our proposed features outperform the original features, as well as some joint density features, in detecting several common steganographic algorithms.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hao Zhang, Hao Zhang, Xijian Ping, Xijian Ping, } "Multi-class Markov models for JPEG steganalysis", Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 887847 (19 July 2013); doi: 10.1117/12.2031619; https://doi.org/10.1117/12.2031619
PROCEEDINGS
5 PAGES


SHARE
RELATED CONTENT

The challenges of rich features in universal steganalysis
Proceedings of SPIE (March 22 2013)
Application of conditional entropy measures to steganalysis
Proceedings of SPIE (February 15 2006)
Steganalysis using logistic regression
Proceedings of SPIE (February 10 2011)
Modern steganalysis can detect YASS
Proceedings of SPIE (January 27 2010)
An artificial neural network for wavelet steganalysis
Proceedings of SPIE (August 30 2005)

Back to Top