Fusion Based Blind Image Steganalysis by Boosting Feature Selection

Authors:
Jing Dong;Xiaochuan Chen;Lei Guo;Tieniu Tan
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 10080;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 10080;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 10080;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 10080
Venue:
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Year:
2008

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Abstract

In this paper, a feature-level fusion based approach is proposed for blind image steganalysis. We choose three types of typical higher-order statistics as the candidate features for fusion and make use of the Boosting Feature Selection (BFS) algorithm as the fusion tool to select a subset of these candidate features as the new fusion feature vector for blind image steganalysis. Support vector machines are then used as the classifier. Experimental results show that the fusion based approach increases the blind detection accuracy and also provides a good generality by identifying an untrained stego-algorithm. Moreover, we evaluate the performance of our candidate features for fusion by making some analysis of the components of the fusion feature vector in our experiments.