  Adversarial training (AT) aims to improve the robustness of deep learning
models by mixing clean data and adversarial examples (AEs). Most existing AT
approaches can be grouped into restricted and unrestricted approaches.
Restricted AT requires a prescribed uniform budget to constrain the magnitude
of the AE perturbations during training, with the obtained results showing high
sensitivity to the budget. On the other hand, unrestricted AT uses
unconstrained AEs, resulting in the use of AEs located beyond the decision
boundary; these overestimated AEs significantly lower the accuracy on clean
data. These limitations mean that the existing AT approaches have difficulty in
obtaining a comprehensively robust model with high accuracy and robustness when
confronting attacks with varying strengths. Considering this problem, this
paper proposes a novel AT approach named blind adversarial training (BAT) to
better balance the accuracy and robustness. The main idea of this approach is
to use a cutoff-scale strategy to adaptively estimate a nonuniform budget to
modify the AEs used in the training, ensuring that the strengths of the AEs are
dynamically located in a reasonable range and ultimately improving the overall
robustness of the AT model. The experimental results obtained using BAT for
training classification models on several benchmarks demonstrate the
competitive performance of this method.
