Repairing Adversarial Texts through Perturbation

TL;DR

This paper introduces a novel runtime method to automatically repair adversarial texts by applying multiple perturbation techniques, successfully restoring correct classification in about 80% of cases and operating efficiently.

Contribution

It proposes a new approach that uses positive perturbations to repair adversarial texts without rejection, improving robustness in NLP models.

Findings

Successfully repairs about 80% of adversarial texts.

Operates in as short as one second per repair.

Effective across multiple NLP models.

Abstract

It is known that neural networks are subject to attacks through adversarial perturbations, i.e., inputs which are maliciously crafted through perturbations to induce wrong predictions. Furthermore, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training. Multiple approaches have been developed to detect and reject such adversarial inputs, mostly in the image domain. Rejecting suspicious inputs however may not be always feasible or ideal. First, normal inputs may be rejected due to false alarms generated by the detection algorithm. Second, denial-of-service attacks may be conducted by feeding such systems with adversarial inputs. To address the gap, in this work, we propose an approach to automatically repair adversarial texts at runtime. Given a text which is suspected to be adversarial, we novelly apply multiple adversarial perturbation methods in a positive way to identify a repair, i.e., a slightly mutated but semantically equivalent text that the neural network correctly classifies. Our approach has been experimented with multiple models trained for natural language processing tasks and the results show that our approach is effective, i.e., it successfully repairs about 80\% of the adversarial texts. Furthermore, depending on the applied perturbation method, an adversarial text could be repaired in as short as one second on average.