Adversarial attack has recently become a tremendous threat to deep learning
models. To improve the robustness of machine learning models, adversarial
training, formulated as a minimax optimization problem, has been recognized as
one of the most effective defense mechanisms. However, the non-convex and
non-concave property poses a great challenge to the minimax training. In this
paper, we empirically demonstrate that the commonly used PGD attack may not be
optimal for inner maximization, and improved inner optimizer can lead to a more
robust model. Then we leverage a learning-to-learn (L2L) framework to train an
optimizer with recurrent neural networks, providing update directions and steps
adaptively for the inner problem. By co-training optimizer's parameters and
model's weights, the proposed framework consistently improves the model
robustness over PGD-based adversarial training and TRADES.

本文介绍针对深度学习模型的对抗攻击防御机制之一 —— 双方博弈的对抗训练。作者将学习 - to-learn 框架应用于对抗训练中，利用递归神经网络训练出更加优秀的内部优化器，同时对优化器参数和模型参数进行联合训练，从而提高了模型的鲁棒性。