Textural features based universal steganalysis

Bin Li; Jiwu Huang; Yun Q. Shi

doi:10.1117/12.765817

18 March 2008 Textural features based universal steganalysis

Bin Li, Jiwu Huang, Yun Q. Shi

Proceedings Volume 6819, Security, Forensics, Steganography, and Watermarking of Multimedia Contents X; 681912 (2008) https://doi.org/10.1117/12.765817
Event: Electronic Imaging, 2008, San Jose, California, United States

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.

Citation Download Citation

Bin Li, Jiwu Huang, and Yun Q. Shi "Textural features based universal steganalysis", Proc. SPIE 6819, Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, 681912 (18 March 2008); https://doi.org/10.1117/12.765817

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available