  Recent studies have revealed the vulnerability of pre-trained language models
to adversarial attacks. Existing adversarial defense techniques attempt to
reconstruct adversarial examples within feature or text spaces. However, these
methods struggle to effectively repair the semantics in adversarial examples,
resulting in unsatisfactory performance and limiting their practical utility.
To repair the semantics in adversarial examples, we introduce a novel approach
named Reactive Perturbation Defocusing (Rapid). Rapid employs an adversarial
detector to identify fake labels of adversarial examples and leverage
adversarial attackers to repair the semantics in adversarial examples. Our
extensive experimental results conducted on four public datasets, convincingly
demonstrate the effectiveness of Rapid in various adversarial attack scenarios.
To address the problem of defense performance validation in previous works, we
provide a demonstration of adversarial detection and repair based on our work,
which can be easily evaluated at https://tinyurl.com/22ercuf8.
