  Meta learning algorithms have been widely applied in many tasks for efficient
learning, such as few-shot image classification and fast reinforcement
learning. During meta training, the meta learner develops a common learning
strategy, or experience, from a variety of learning tasks. Therefore, during
meta test, the meta learner can use the learned strategy to quickly adapt to
new tasks even with a few training samples. However, there is still a dark side
about meta learning in terms of reliability and robustness. In particular, is
meta learning vulnerable to adversarial attacks? In other words, would a
well-trained meta learner utilize its learned experience to build wrong or
likely useless knowledge, if an adversary unnoticeably manipulates the given
training set? Without the understanding of this problem, it is extremely risky
to apply meta learning in safety-critical applications. Thus, in this paper, we
perform the initial study about adversarial attacks on meta learning under the
few-shot classification problem. In particular, we formally define key elements
of adversarial attacks unique to meta learning and propose the first attacking
algorithm against meta learning under various settings. We evaluate the
effectiveness of the proposed attacking strategy as well as the robustness of
several representative meta learning algorithms. Experimental results
demonstrate that the proposed attacking strategy can easily break the meta
learner and meta learning is vulnerable to adversarial attacks. The
implementation of the proposed framework will be released upon the acceptance
of this paper.
