LSB steganalysis using support vector regression

Author

Lin, Erwei ; Woertz, Edward ; Kam, Moshe

fYear

2004

fDate

10-11 June 2004

Firstpage

95

Lastpage

100

Abstract

We describe a method of detecting the existence of messages, which are randomly scattered in the least significant bits (LSB) of both 24-bit RGB color and 8-bit grayscale images. The method is based on gathering and inspecting a set of image relevant features from the pixel groups of the stego-image, whose similarities and correlations change with different ratios of LSB embedding. The proposed detection scheme is based on support vector regression (SVR). It is shown that the measurement of a selected set of features forms a multidimensional feature space which allows estimation of the length of hidden messages embedded in the LSB of cover-images with high precision.

Keywords

data encapsulation; feature extraction; regression analysis; security of data; support vector machines; watermarking; LSB; image features; information detection; least significant bit; multidimensional feature space; steganalysis; support vector regression; Cryptography; Extraterrestrial measurements; Gray-scale; Histograms; Length measurement; Multidimensional systems; Pixel; Scattering; Steganography; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Assurance Workshop, 2004. Proceedings from the Fifth Annual IEEE SMC

Print_ISBN

0-7803-8572-1

Type

conf

DOI

10.1109/IAW.2004.1437803

Filename

1437803