Image complexity and feature mining for steganalysis of least significant bit matching steganography

Authors:
Qingzhong Liu;Andrew H. Sung;Bernardete Ribeiro;Mingzhen Wei;Zhongxue Chen;Jianyun Xu
Affiliations:
New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA;New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA;Department of Informatics Engineering, University of Coimbra, Portugal;University of Missouri-Rolla, 1870 Miner Circle, Rolla, MO 65409, USA;Department of Statistical Science, Southern Methodist University, Dallas, TX 75275-0332, USA;Microsoft Corporation, One Microsoft Way, Redmond, WA 98052-6399, USA
Venue:
Information Sciences: an International Journal
Year:
2008

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Abstract

The information-hiding ratio is a well-known metric for evaluating steganalysis performance. In this paper, we introduce a new metric of image complexity to enhance the evaluation of steganalysis performance. In addition, we also present a scheme of steganalysis of least significant bit (LSB) matching steganography, based on feature mining and pattern recognition techniques. Compared to other well-known methods of steganalysis of LSB matching steganography, our method performs the best. Results also indicate that the significance of features and the detection performance depend not only on the information-hiding ratio, but also on the image complexity.