Paper Info
Title
Steganalysis of LSB matching based on the sum features of average co-occurrence matrix using image estimation
Abstract
A new LSB matching steganalysis scheme for gray images is proposed in this paper. This method excavates the relevance between pixels in the LSB matching stego image from the co-occurrence matrix. This method can acquire high accuracy near to 100% at high embedding rate. In order to increase the accuracy at low embedding rate, we strengthen the differences between the cover image and the stego image to improve the performance of our scheme. Two 8 dimensional feature vectors are extracted separately from the test image and the restoration image, and then the combining 16 dimensional feature vector is used for steganalysis with the FISHER linear classification. Experimental results show that the detection accuracy of this method is above 90% with the embedding rate of 25%; even when the embedding rate is 10%, the detection accuracy reaches 80%.Experiments show that this method is more reliable than other state-of-art methods.
Year
DOI
Venue
2012
10.1007/978-3-642-40099-5_4
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Keywords
Field
DocType
embedding rate,detection accuracy,dimensional feature vector,high embedding rate,stego image,high accuracy,cover image,average co-occurrence matrix,image estimation,gray image,restoration image,sum feature,test image
Steganography,Feature vector,Co-occurrence matrix,Pattern recognition,Computer science,Pixel,Artificial intelligence,Steganalysis,Image restoration,Linear classifier,Standard test image
Conference
Volume
Issue
ISSN
7809 LNCS
null
16113349
Citations 
PageRank 
References 
1
0.50
8
Authors
4
Name
Order
Citations
PageRank
Yanqing Guo13912.24
Xiang-Wei Kong221215.09
Bo Wang310.50
Qian Xiao410.50