Blind quantitative steganalysis based on feature fusion and gradient boosting

Authors:
Qingxiao Guan;Jing Dong;Tieniu Tan
Affiliations:
National Laborary of Pattern Recognition, CAS Institute of Automation and Department of Automation, University of Science and Technology of China;National Laborary of Pattern Recognition, CAS Institute of Automation;National Laborary of Pattern Recognition, CAS Institute of Automation
Venue:
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Year:
2010

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Abstract

Blind quantitative steganalysis is about revealing more details about hidden information without any prior knowledge of steganograghy. Machine learning can be used to estimate some properties of hidden message for blind quantitative steganalysis. We propose a quantitative steganalysis method based on fusion of different steganalysis features and the estimator relies on gradient boosting. Experimental result shows that our proposed method has good performance for quantitative steganalysis.