  Conventional steganalysis detects the presence of steganography within single
objects. In the real-world, we may face a complex scenario that one or some of
multiple users called actors are guilty of using steganography, which is
typically defined as the Steganographer Identification Problem (SIP). One might
use the conventional steganalysis algorithms to separate stego objects from
cover objects and then identify the guilty actors. However, the guilty actors
may be lost due to a number of false alarms. To deal with the SIP, most of the
state-of-the-arts use unsupervised learning based approaches. In their
solutions, each actor holds multiple digital objects, from which a set of
feature vectors can be extracted. The well-defined distances between these
feature sets are determined to measure the similarity between the corresponding
actors. By applying clustering or outlier detection, the most suspicious
actor(s) will be judged as the steganographer(s). Though the SIP needs further
study, the existing works have good ability to identify the steganographer(s)
when non-adaptive steganographic embedding was applied. In this chapter, we
will present foundational concepts and review advanced methodologies in SIP.
This chapter is self-contained and intended as a tutorial introducing the SIP
in the context of media steganography.
