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Proceedings Paper

Textural features based universal steganalysis
Author(s): Bin Li; Jiwu Huang; Yun Q. Shi
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Paper Abstract

This paper takes the task of image steganalysis as a texture classification problem. The impact of steganography to an image is viewed as the alteration of image texture in a fine scale. Specifically, stochastic textures are more likely to appear in a stego image than in a cover image from our observation and analysis. By developing a feature extraction technique previously used in texture classification, we propose a set of universal steganalytic features, which are extracted from the normalized histograms of the local linear transform coefficients of an image. Extensive experiments are conducted to make comparison of our proposed feature set with some existing universal steganalytic feature sets on gray-scale images by using Fisher Linear Discriminant (FLD). Some classical non-adaptive spatial domain steganographic algorithms, as well as some newly presented adaptive spatial domain steganographic algorithms that have never been reported to be broken by any universal steganalytic algorithm, are used for benchmarking. We also report the detection performance on JPEG steganography and JPEG2000 steganography. The comparative experimental results show that our proposed feature set is very effective on a hybrid image database.

Paper Details

Date Published: 18 March 2008
PDF: 12 pages
Proc. SPIE 6819, Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, 681912 (18 March 2008); doi: 10.1117/12.765817
Show Author Affiliations
Bin Li, Sun Yat-Sen Univ. (China)
New Jersey Institute of Technology (United States)
Jiwu Huang, Sun Yat-Sen Univ. (China)
Yun Q. Shi, New Jersey Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6819:
Security, Forensics, Steganography, and Watermarking of Multimedia Contents X
Edward J. Delp; Ping Wah Wong; Jana Dittmann; Nasir D. Memon, Editor(s)

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