Unsupervised universal steganalyzer for high-dimensional steganalytic features

Xiaodan Hou; Tao Zhang

doi:10.1117/1.JEI.25.6.063016

8 December 2016 Unsupervised universal steganalyzer for high-dimensional steganalytic features

Xiaodan Hou, Tao Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 6, 063016 (December 2016). https://doi.org/10.1117/1.JEI.25.6.063016

Abstract

The research in developing steganalytic features has been highly successful. These features are extremely powerful when applied to supervised binary classification problems. However, they are incompatible with unsupervised universal steganalysis because the unsupervised method cannot distinguish embedding distortion from varying levels of noises caused by cover variation. This study attempts to alleviate the problem by introducing similarity retrieval of image statistical properties (SRISP), with the specific aim of mitigating the effect of cover variation on the existing steganalytic features. First, cover images with some statistical properties similar to those of a given test image are searched from a retrieval cover database to establish an aided sample set. Then, unsupervised outlier detection is performed on a test set composed of the given test image and its aided sample set to determine the type (cover or stego) of the given test image. Our proposed framework, called SRISP-aided unsupervised outlier detection, requires no training. Thus, it does not suffer from model mismatch mess. Compared with prior unsupervised outlier detectors that do not consider SRISP, the proposed framework not only retains the universality but also exhibits superior performance when applied to high-dimensional steganalytic features.

Citation Download Citation

Xiaodan Hou and Tao Zhang "Unsupervised universal steganalyzer for high-dimensional steganalytic features," Journal of Electronic Imaging 25(6), 063016 (8 December 2016). https://doi.org/10.1117/1.JEI.25.6.063016

Received: 19 September 2016; Accepted: 14 November 2016; Published: 8 December 2016

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