Title :
LSB steganalysis using support vector regression
Author :
Lin, Erwei ; Woertz, Edward ; Kam, Moshe
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;
Conference_Titel :
Information Assurance Workshop, 2004. Proceedings from the Fifth Annual IEEE SMC
Print_ISBN :
0-7803-8572-1
DOI :
10.1109/IAW.2004.1437803
