  Deep learning models continue to advance in accuracy, yet they remain
vulnerable to adversarial attacks, which often lead to the misclassification of
adversarial examples. Adversarial training is used to mitigate this problem by
increasing robustness against these attacks. However, this approach typically
reduces a model's standard accuracy on clean, non-adversarial samples. The
necessity for deep learning models to balance both robustness and accuracy for
security is obvious, but achieving this balance remains challenging, and the
underlying reasons are yet to be clarified. This paper proposes a novel
adversarial training method called Adversarial Feature Alignment (AFA), to
address these problems. Our research unveils an intriguing insight:
misalignment within the feature space often leads to misclassification,
regardless of whether the samples are benign or adversarial. AFA mitigates this
risk by employing a novel optimization algorithm based on contrastive learning
to alleviate potential feature misalignment. Through our evaluations, we
demonstrate the superior performance of AFA. The baseline AFA delivers higher
robust accuracy than previous adversarial contrastive learning methods while
minimizing the drop in clean accuracy to 1.86% and 8.91% on CIFAR10 and
CIFAR100, respectively, in comparison to cross-entropy. We also show that joint
optimization of AFA and TRADES, accompanied by data augmentation using a recent
diffusion model, achieves state-of-the-art accuracy and robustness.
