Steganalysis of Multi-Class JPEG Images Based on Expanded Markov Features and Polynomial Fitting

被引：4
|
作者：
Liu, Qingzhong [1 ,2 ]
Sung, Andrew H. [1 ,2 ]
Ribeiro, Bernardete M. [3 ]
Ferreira, Rita [3 ]
机构：
[1] New Mexico Inst Min & Technol, Inst Complex Addit Syst Anal, Socorro, NM 87801 USA
[2] New Mexico Inst Min & Technol, Dept Comp Sci, Socorro, NM 87801 USA
[3] Univ Coimbra, Dept Informat Engn, Coimbra, Portugal
来源：
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 | 2008年
关键词：
D O I：
10.1109/IJCNN.2008.4634274
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this article, based on the Markov approach proposed by shi et al [1], we expand it to the inter-blocks of the DCT domain, calculate the difference of the expanded Markov features between the testing image and the calibrated version, and combine these difference features and the polynomial fitting features on the histogram of the DCT coefficients as detectors. We reasonably improve the detection performance in multi-class JPEG images. We also compare the steganalysis performance among the feature reduction/selection methods based on principal component analysis, singular value decomposition, and Fisher's linear discriminant.
引用
收藏
页码：3352 / +
页数：3
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