  Applying neural network (NN) methods in games can lead to various new and
exciting game dynamics not previously possible. However, they also lead to new
challenges such as the lack of large, clean datasets, varying player skill
levels, and changing gameplay strategies. In this paper, we focus on the
adversarial player strategy aspect in the game iNNk, in which players try to
communicate secret code words through drawings with the goal of not being
deciphered by a NN. Some strategies exploit weaknesses in the NN that
consistently trick it into making incorrect classifications, leading to
unbalanced gameplay. We present a method that combines transfer learning and
ensemble methods to obtain a data-efficient adaptation to these strategies.
This combination significantly outperforms the baseline NN across all
adversarial player strategies despite only being trained on a limited set of
adversarial examples. We expect the methods developed in this paper to be
useful for the rapidly growing field of NN-based games, which will require new
approaches to deal with unforeseen player creativity.
