Abstract
Subtractive pixel adjacency matrix (SPAM) features, introduced by Pevn’y et al. as a type of Markov chain features, are widely used for blind steganalysis in the spatial domain. In this paper, we present three improvements to SPAM as follows: 1) new features based on parallel subtractive pixels are added to the SPAM features, which only refer to collinear subtractive pixels; 2) features are extracted not only from the spatial image, but also from its grayscale-inverted image, making the feature matrices symmetrical and reducing their dimensionality by about half; and 3) a new kind of adjacency matrix is used, thereby reducing about 3/4 of the dimensionality of the features. Experimental results show that these methods for dimensionality reduction are very effective and that the proposed features outperform SPAM.
This is a preview of subscription content, log in via an institution to check access.
Access this article
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
Similar content being viewed by others
References
Lyu S, Farid H. Steganalysis using higher-order image statistics. IEEE Trans Inf Forensic Secur, 2006, 1: 111–119
Goljan M, Fridrich J, Holotyak T. New blind steganalysis and its implications. In: Proceedings of SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VIII, San Jose, 2006. 1–13
Harmsen J J, Pearlman W A. Steganalysis of additive noise moderable information hiding. In: Proceedings of SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents VI, Santa Clara, 2003. 131–142
Wang Y, Moulin P. Optimized feature extraction for learning-based image steganalysis. IEEE Trans Inf Forensic Secur, 2007, 2: 31–45
Shi Y Q, Chen C, Chen W. A Markov process based approach to effective attacking JPEG steganography. In: Camenisch J L, Collberg C S, Johnson N F, et al., eds. Information Hiding, 8th International Workshop. Berlin: Springer-Verlag, 2006. 249–264
Pevný T, Bas P, Fridrich J. Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensic Secur, 2010, 5: 215–224
Fridrich J, Kodovský J, Holub V, et al. Steganalysis of content-adaptive steganography in spatial domain. In: Filler T, Pevný T, Ker A, et al., eds. Information Hiding, 13th International Workshop. Berlin: Springer-Verlag, 2011. 101–11
Cancelli G, Doërr G, Cox I, et al. Detection of ±1 steganography based on the amplitude of histogram local extreme. I In: Proceedings of the 15th IEEE International Conference on Image Processing, San Diego, 2008. 12–15
Pevný T, Fridrich J. Merging Markov and DCT features for multi-class JPEG steganalysis. In: Proceedings of SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents IX, San Jose, 2007. 1–14
Solanki K, Sarkar A, Manjunath B S. YASS: Yet another steganographic scheme that resist blind steganalysis. In: Furon T, Cayre F, Doërr G, et al., eds. Information Hiding, 9th International Workshop. Berlin: Springer-Verlag, 2007. 16–31
Sarkar A, Solanki K, Manjunath B S. Further study on YASS: Steganography based on randomized embedding to resist blind steganalysis. In: Proceedings of SPIE, Electronic Imaging, Security, Forensics, Steganography, andWatermarking of Multimedia Contents X, San Jose, 2008. 16–31
Pevný T, Filler T, Bas P. Using high-dimensional image models to perform highly undetectable steganography. In: Fong P, Böhme R, Safavinaini R, eds. Information Hiding, 12th International Workshop. Berlin: Springer-Verlag, 2010. 161–177
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, H., Ping, X., Xu, M. et al. Steganalysis by subtractive pixel adjacency matrix and dimensionality reduction. Sci. China Inf. Sci. 57, 1–7 (2014). https://doi.org/10.1007/s11432-013-4793-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-013-4793-x