DocumentCode
1872175
Title
Blind image steganalysis based on run-length histogram analysis
Author
Dong, Jing ; Tan, Tieniu
Author_Institution
Inst. of Autom., Chinese Acad. of Sci., Beijing
fYear
2008
fDate
12-15 Oct. 2008
Firstpage
2064
Lastpage
2067
Abstract
In this paper, a new, simple but effective method is proposed for blind image steganalysis, which is based on run-length histogram analysis. Higher-order statistics of characteristic functions of three types of image run-length histograms are selected as features. Support vector machine is used as classifier. Experimental results demonstrate that the proposed scheme significantly outperforms prior arts in detection accuracy and generality.
Keywords
higher order statistics; image classification; steganography; support vector machines; SVM classifier; blind image steganalysis; higher-order statistics; run-length histogram analysis; support vector machine; Feature extraction; Histograms; Image analysis; Laboratories; Pattern analysis; Steganography; Supervised learning; Support vector machine classification; Support vector machines; Wavelet coefficients; Blind steganalysis; run length histogram; supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on
Conference_Location
San Diego, CA
ISSN
1522-4880
Print_ISBN
978-1-4244-1765-0
Electronic_ISBN
1522-4880
Type
conf
DOI
10.1109/ICIP.2008.4712192
Filename
4712192
Link To Document