Image Steganalysis Based on Spatial Domain and DWT Domain Features

Author

Liu, Changxin ; Ouyang, Chunjuan ; Guo, Ming ; Chen, Huijuan

Author_Institution

Dept. of Comput. Sci., Jinggangshan Univ., Ji´´an, China

Volume

1

fYear

2010

fDate

24-25 April 2010

Firstpage

329

Lastpage

331

Abstract

In this paper, a new image steganalysis method was proposed based on image gradient energy and entropy features, together with Fraid´s proposed wavelet subband coefficients and higher-order statistics of linear prediction error features. We get 74-dimensional features extraction from images. The support vector machines (SVM) is used to class the images. Experimental results show the method can improve the detection rate compared with Fraid´s algorithm with a lower false negative rate.

Keywords

discrete wavelet transforms; entropy; feature extraction; gradient methods; higher order statistics; image classification; image coding; steganography; support vector machines; 74-dimensional feature extraction; DWT domain feature; false negative rate; higher-order statistics; image classification; image gradient energy feature; image gradient entropy feature; image steganalysis method; linear prediction error features; spatial domain feature; support vector machines; wavelet subband coefficients; Computer science; Computer security; Discrete wavelet transforms; Entropy; Feature extraction; Higher order statistics; Pixel; Support vector machines; Wavelet domain; Wireless communication; gradient energy; higher-order statistics; image entropy; steganalysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Networks Security Wireless Communications and Trusted Computing (NSWCTC), 2010 Second International Conference on

Conference_Location

Wuhan, Hubei

Print_ISBN

978-0-7695-4011-5

Electronic_ISBN

978-1-4244-6598-9

Type

conf

DOI

10.1109/NSWCTC.2010.271

Filename

5480720