This papers consists of two parts. The first is a critical review of prior
art on adversarial learning, identifying some significant limitations of
previous works. The second part is an experimental study considering
adversarial active learning and an investigation of the efficacy of a mixed
sample selection strategy for combating an adversary who attempts to disrupt
the classifier learning