  Adversarial training (AT) is always formulated as a minimax problem, of which
the performance depends on the inner optimization that involves the generation
of adversarial examples (AEs). Most previous methods adopt Projected Gradient
Decent (PGD) with manually specifying attack parameters for AE generation. A
combination of the attack parameters can be referred to as an attack strategy.
Several works have revealed that using a fixed attack strategy to generate AEs
during the whole training phase limits the model robustness and propose to
exploit different attack strategies at different training stages to improve
robustness. But those multi-stage hand-crafted attack strategies need much
domain expertise, and the robustness improvement is limited. In this paper, we
propose a novel framework for adversarial training by introducing the concept
of "learnable attack strategy", dubbed LAS-AT, which learns to automatically
produce attack strategies to improve the model robustness. Our framework is
composed of a target network that uses AEs for training to improve robustness
and a strategy network that produces attack strategies to control the AE
generation. Experimental evaluations on three benchmark databases demonstrate
the superiority of the proposed method. The code is released at
https://github.com/jiaxiaojunQAQ/LAS-AT.
