  Deep learning models are known to be vulnerable to adversarial examples. A
practical adversarial attack should require as little as possible knowledge of
attacked models. Current substitute attacks need pre-trained models to generate
adversarial examples and their attack success rates heavily rely on the
transferability of adversarial examples. Current score-based and decision-based
attacks require lots of queries for the attacked models. In this study, we
propose a novel adversarial imitation attack. First, it produces a replica of
the attacked model by a two-player game like the generative adversarial
networks (GANs). The objective of the generative model is to generate examples
that lead the imitation model returning different outputs with the attacked
model. The objective of the imitation model is to output the same labels with
the attacked model under the same inputs. Then, the adversarial examples
generated by the imitation model are utilized to fool the attacked model.
Compared with the current substitute attacks, imitation attacks can use less
training data to produce a replica of the attacked model and improve the
transferability of adversarial examples. Experiments demonstrate that our
imitation attack requires less training data than the black-box substitute
attacks, but achieves an attack success rate close to the white-box attack on
unseen data with no query.
