The Cover-Source Mismatch (CSM) has been long recognized as a major problem in modern steganography and steganalysis. Indeed, while a vast majority of works in steganography and steganalysis had been tailored to a specific reference database, namely BOSSbase, recent works show that, because of CSM, the results may greatly differ when changing this dataset. Although the CSM has already been the subject of several publications, these prior works investigated only a few elements in a limited setup. The goal of the current paper is to study the effects of the CSM in a more comprehensive manner and then to examine and compare different strategies for mitigating it. It first defines two different parameters, the source difficulty and the source inconsistency, which are involved in the CSM. Then, using different steganographic schemes and feature sets, it aims at providing a systematic study regarding the various factors that can give birth to CSM for image steganalysis. Finally, two practical ways to mitigate the CSM, using training techniques promoting either diversity of different sources or the specificity of one targeted source which is beforehand identified by training a multi-class classifier, are presented and their performances are compared for different training set sizes.

盖源不匹配（CSM）长期以来一直被认为是现代隐写术和隐写分析中的一个主要问题。确实，尽管隐写术和隐写分析的绝大多数工作都是针对特定的参考数据库（即BOSSbase）量身定制的，但最近的工作表明，由于CSM的原因，更改此数据集时结果可能会大大不同。尽管CSM已经成为几本出版物的主题，但是这些先前的工作仅在有限的范围内研究了一些要素。本文的目的是更全面地研究CSM的效果，然后研究和比较缓解CSM的不同策略。它首先定义了CSM中涉及的两个不同参数，即源难度和源不一致。然后，使用不同的隐写方案和功能集，它的目的是就可导致CSM进行图像隐写分析的各种因素提供系统的研究。最后，提出了两种减轻CSM的实用方法，它们使用了促进不同来源的多样性或通过训练多类分类器预先确定的一种目标来源的特异性的培训技术，并比较了它们在不同训练集大小下的表现。