Previous work on adversarially robust neural networks requires large training sets and computationally expensive training procedures.   On the other hand, few-shot learning methods are highly vulnerable to adversarial examples.   The goal of our work is to produce networks which both perform well at few-shot tasks and are simultaneously robust to adversarial examples.   We adapt adversarial training for meta-learning, we adapt robust architectural features to small networks for meta-learning, we test pre-processing defenses as an alternative to adversarial training for meta-learning, and we investigate the advantages of robust meta-learning over robust transfer-learning for few-shot tasks.   This work provides a thorough analysis of adversarially robust methods in the context of meta-learning, and we lay the foundation for future work on defenses for few-shot tasks.
We develop meta-learning methods for adversarially robust few-shot learning. This paper presents a method that enhances the robustness of few-shot learning by introducing adversarial query data attack in the inner-task fine-tuning phase of a meta-learning algorithm. The authors of this paper propose a novel approach for training a robust few-shot model. 