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Steganography forensics method for detecting least significant bit replacement attack

[+] Author Affiliations
Xiaofeng Wang

Xi’an University of Technology, School of Science and Shaanxi Key Laboratory for Network Computing and Security Technology, No. 5 South Jinhua Road, Xi’an 710048, China

Chengcheng Wei

Xi’an Technological University, Industrial Center, No. 4 North Jinhua Road, Xi’an 710032, China

Xiao Han

Xi’an University of Technology, School of Science and Shaanxi Key Laboratory for Network Computing and Security Technology, No. 5 South Jinhua Road, Xi’an 710048, China

J. Electron. Imaging. 24(1), 013016 (Jan 20, 2015). doi:10.1117/1.JEI.24.1.013016
History: Received April 9, 2014; Accepted December 17, 2014
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Abstract.  We present an image forensics method to detect least significant bit replacement steganography attack. The proposed method provides fine-grained forensics features by using the hierarchical structure that combines pixels correlation and bit-planes correlation. This is achieved via bit-plane decomposition and difference matrices between the least significant bit-plane and each one of the others. Generated forensics features provide the susceptibility (changeability) that will be drastically altered when the cover image is embedded with data to form a stego image. We developed a statistical model based on the forensics features and used least square support vector machine as a classifier to distinguish stego images from cover images. Experimental results show that the proposed method provides the following advantages. (1) The detection rate is noticeably higher than that of some existing methods. (2) It has the expected stability. (3) It is robust for content-preserving manipulations, such as JPEG compression, adding noise, filtering, etc. (4) The proposed method provides satisfactory generalization capability.

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Citation

Xiaofeng Wang ; Chengcheng Wei and Xiao Han
"Steganography forensics method for detecting least significant bit replacement attack", J. Electron. Imaging. 24(1), 013016 (Jan 20, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.1.013016


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