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
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;
Conference_Titel :
Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-1765-0
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2008.4712192
