RAAT harmonizes clean accuracy and adversarial robustness by using fixed reduced perturbations for boundary samples and Domain Interpolation Consistency Adversarial Regularization to align input and latent spaces.
Prac- tical black-box attacks against machine learning
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Robust Alignment: Harmonizing Clean Accuracy and Adversarial Robustness in Adversarial Training
RAAT harmonizes clean accuracy and adversarial robustness by using fixed reduced perturbations for boundary samples and Domain Interpolation Consistency Adversarial Regularization to align input and latent spaces.