  Adversarial training is often formulated as a min-max problem, however,
concentrating only on the worst adversarial examples causes alternating
repetitive confusion of the model, i.e., previously defended or correctly
classified samples are not defensible or accurately classifiable in subsequent
adversarial training. We characterize such non-ignorable samples as "hiders",
which reveal the hidden high-risk regions within the secure area obtained
through adversarial training and prevent the model from finding the real worst
cases. We demand the model to prevent hiders when defending against adversarial
examples for improving accuracy and robustness simultaneously. By rethinking
and redefining the min-max optimization problem for adversarial training, we
propose a generalized adversarial training algorithm called Hider-Focused
Adversarial Training (HFAT). HFAT introduces the iterative evolution
optimization strategy to simplify the optimization problem and employs an
auxiliary model to reveal hiders, effectively combining the optimization
directions of standard adversarial training and prevention hiders. Furthermore,
we introduce an adaptive weighting mechanism that facilitates the model in
adaptively adjusting its focus between adversarial examples and hiders during
different training periods. We demonstrate the effectiveness of our method
based on extensive experiments, and ensure that HFAT can provide higher
robustness and accuracy.
