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.

在现实世界中的隐写分析应用中，由来自某个来源的一组训练数据构建的传统隐写分析方法可以应用于检测来自另一个新的不同来源的数据。在这种情况下，隐身分析仪将面临一个严重的问题，即训练数据和测试数据不再具有相同的分布，因此检测性能将迅速下降。针对这一问题，本文提出了一种新的具有结构保留的转移子空间学习方法，用于图像隐写分析。目的是减轻训练数据和测试数据之间的不匹配，以提高检测性能。特别，对于训练和测试数据，学习判别投影矩阵，以使两组的投影数据都位于一个公共子空间中，在该子空间中，可以通过训练数据的组合来线性地重构每个样本。这样，训练集和测试集之间的差异就减小了。此外，为了保留投影子空间中特征的结构信息，将基于Frobenius范数的正则化项引入目标函数。此外，为了减轻噪声和离群值的负面影响，引入了结构稀疏的误差矩阵来对噪声和离群值信息进行建模。通过交替优化算法可以有效地解决所提出方法的问题。