In the past few years, we have witnessed a number of powerful steganalysis technique proposed in the literature. These techniques could be categorized as either specific or universal. Each category of techniques has a set of advantages and disadvantages. A steganalysis technique specific to a steganographic embedding technique would perform well when tested only on that method and might fail on all others. On the other hand, universal steganalysis methods perform less accurately overall but provide acceptable performance in many cases. In practice, since the steganalyst will not be able to know what steganographic technique is used, it has to deploy a number of techniques on suspected images. In such a setting the most important question that needs to be answered is: What should the steganalyst do when the decisions produced by different steganalysis techniques are in contradiction? In this work, we propose and investigate the use of information fusion methods to aggregate the outputs of multiple steganalysis techniques. We consider several fusion rules that are applicable to steganalysis, and illustrate, through a number of case studies, how
steganalyzers with improved performance can be designed. It is shown that fusion techniques increase detection accuracy and offer scalability, by enabling seamless integration of new steganalysis techniques.