关键词: 图像隐写, 残差网络, 集成分类器, 深度学习 

Abstract: The traditional steganalysis applies Ｒich Model features through Ensemble Classifier to achieve high detection performance． While the deep learning framework shows more powerful detection performance than traditional ones in steganalysis so far． It has been shown that the deep residual network is similar to the ensemble classifier． To confirm whether or not Xu's network，based on the steganalyzer of deep residual network which we find is not deep enough，is characteristic of the features mentioned above，we introduce deeper bottleneck architecture and reproduction of building blocks to expand them respectively，and we get four variants—bottleneck network，30layer ＲesNet，40layer ＲesNet and 50layer ＲesNet． In this article，three experiments are introduced． The first is to train the Xu's network and the variants in order to obtain the optimal models． As a result，we found that the performance of deeper network is not better than that of Xu's network． The second is to remove a building block， proving that the path in the residual network does not depend on each other． The third is to re-order some building blocks，indicating that the residual network to a certain extent can be re-configured． Finally we conclude that Xu's network is also similar to ensembles of relatively shallow networks． 

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