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Proceedings Paper

Steganalysis based on reducing the differences of image statistical characteristics
Author(s): Ran Wang; Shaozhang Niu; Xijian Ping; Tao Zhang
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Paper Abstract

Compared with the process of embedding, the image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger withinclass scatter distances and smaller between-class scatter distances. As a result, the steganalysis features will be inseparate caused by the differences of image statistical characteristics. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are segmented to several sub-images according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. The theoretical analysis and experimental results can demonstrate the validity of the framework.

Paper Details

Date Published: 10 April 2018
PDF: 12 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151J (10 April 2018); doi: 10.1117/12.2304572
Show Author Affiliations
Ran Wang, Beijing Univ. of Posts and Telecommunications (China)
Zhengzhou Information Science and Technology Institute (China)
Shaozhang Niu, Beijing Univ. of Posts and Telecommunications (China)
Xijian Ping, Zhengzhou Information Science and Technology Institute (China)
Zhengzhou Shengda Univ. of Economics, Business and Management (China)
Tao Zhang, Zhengzhou Information Science and Technology Institute (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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