Transfer subspace learning based on structure preservation for JPEG image mismatched steganalysis |
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Affiliation: | 1. Beijing University of Posts and Telecommunications, Beijing, China;2. Alibaba Group, Beijing, China;3. Chongqing University of Posts and Telecommunications, Chongqing, China;4. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. Centre for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences. School of Artificial Intelligence, University of Chinese Academy of Sciences, China;1. Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education, Wuhan University, Wuhan, Hubei 430072, China;2. School of Cyber Science and Engineering, Wuhan University, Wuhan, Hubei 430072, China |
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Abstract: | In real-world steganalysis applications, the traditional steganalysis methods built by a set of training data coming from a source may be applied to detect data from another new different source. In this case, the steganalyzers will face a serious problem that training data and test data are no longer subjected to the same distribution, and thus the detection performance would degrade rapidly. To address this problem, a novel transfer subspace learning method with structure preservation for image steganalysis is proposed in this paper. It aims to alleviate the mismatch between the training and test data so as to improve the detection performance. Specifically, a discriminant projection matrix is learned for the training and test data such that the projected data of both sets lie in a common subspace where each sample can be linearly reconstructed by a combination of the training data. In this way, the difference between the training and test sets is decreased. Further, in order to preserve the structure information of features in the projection subspace, a Frobenius-norm based regularization term is introduced into the objective function. Moreover, to mitigate the negative impacts of noises and outliers, a structurally sparse error matrix is introduced to model the noise and outlier information. The formulation of the proposed method can be efficiently solved by an alternating optimization algorithm. The extensive experiments compared with prior arts show the validity of the proposed method for JPEG image mismatched steganalysis. |
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Keywords: | Mismatch Steganalysis JPEG image Transfer subspace learning Structure preservation |
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