基于分组卷积和快照集成的图像隐写分析方法

现存的大多数隐写分析方法的泛化能力较弱，无法对未知隐写算法有效检测，使得其分类的准确性在实际运用过程中大幅度降低。针对这个问题，提出了一种基于分组卷积和快照集成的图像隐写分析方法(snapshot ensembling steganalysis network, SENet)。首先，残差卷积块和分组卷积块对图像的特征进行提取并利用；其次，在每个训练周期中得到性能最好的模型作为快照模型；最后将选定的快照模型进行集成后对图像进行分类。该方法应用分组卷积和快照集成的技术，避免了传统集成方法的高训练成本以及单一分类器泛化能力有限的问题。实验结果表明，该方法可以提升隐写分析模型的准确率，并且在训练集和测试集失配时，也能够有效地进行分类，具有较高的模型泛化能力。

Image steganalysis based on block convolution and snapshot ensembling

Most of the existing steganalysis methods have weak generalization ability and cannot effectively detect unknown steganalysis algorithms, which makes the accuracy of their classification greatly reduced in the practical application process. To solve this problem, this paper proposed an image steganalysis method based on group convolution and snapshot ensembling SENet. Firstly, it used residual convolution block and group convolution block to extract the features of the image. Secondly, it obtained the model with the best performance as the snapshot model in each training period. Finally, it integrated the selected snapshot models to classify the images. This method used the techniques of group convolution and snapshot ensembling to avoid the high training cost of traditional integration methods and the limited generalization ability of a single classifier. Experimental results show that this method can improve the accuracy of steganographic analysis model, and can effectively classify when the training set and test set are mismatched, and has high model generalization ability.