We propose a new paradigm for blind, universal, steganalysis in the case when multiple actors transmit multiple
objects, with guilty actors including some stego objects in their transmissions. The method is based on clustering
rather than classification, and it is the actors which are clustered rather than their individual transmitted objects.
This removes the need for training a classifier, and the danger of training model mismatch. It effectively judges
the behaviour of actors by assuming that most of them are innocent: after performing agglomerative hierarchical
clustering, the guilty actor(s) are clustered separately from the innocent majority. A case study shows that this
works in the case of JPEG images. Although it is less sensitive than steganalysis based on specifically-trained
classifiers, it requires no training, no knowledge of the embedding algorithm, and attacks the pooled steganalysis