| Author Name | Affiliation | | Li-Na Wang | Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education,Wuhan University, Wuhan 430072, China
School of Computer, Wuhan University, Wuhan 430072, China | | Min-Jie Wang | Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education,Wuhan University, Wuhan 430072, China
School of Computer, Wuhan University, Wuhan 430072, China | | Ting-Ting Zhu | Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education,Wuhan University, Wuhan 430072, China
School of Computer, Wuhan University, Wuhan 430072, China | | Qing Dou | Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education,Wuhan University, Wuhan 430072, China
School of Computer, Wuhan University, Wuhan 430072, China |
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| Abstract: |
| In the image steganalysis, the training samples often determine the performance of the model when the features and classification are in the same condition. However the existing research on steganalysis lacks the in-depth study of the classifier’s training method which may deeply influence the detection performance. This paper provides an optimization of universal steganalysis based on the boundary samples classification concerning about image steganalysis. This paper proposes a strategy of selecting boundary samples in steganalysis and divides the training samples into good samples, poor samples and boundary samples three categories and then chose the optimal threshold to get boundary samples through experiments. The experimental results show the effectiveness of boundary sample, which dramatically improve detection capability especially for the low embedding rate Stego-image. |
| Key words: image steganalysis digital forensics support vector machine (SVM) boundary samples |
| DOI：10.11916/j.issn.1005-9113.2014.06.011 |
| Clc Number:TP309 |
| Fund: |
