Natural Language Adversarial Attacks and Defenses in Word Level
Year: 2019
Type: preprint
Abstract: Up until recent two years, inspired by the big amount of research aboutadversarial example in the field of computer vision, there has been a growing
interest in adversarial attacks for Natural Language Processing (NLP). What
followed was a very few works of adversarial defense for NLP. However, there
exists no defense method against the successful synonyms substitution based
attacks that aim to satisfy all the lexical, grammatical, semantic constraints
and thus are hard to perceived by humans. To fill this gap, we postulate the
generalization of the model leads to the existence of adversarial examples, and
propose an adversarial defense method called Synonyms Encoding Method (SEM),
which inserts an encoder before the input layer of the model and then trains
the model to eliminate adversarial perturbations. Extensive experiments
demonstrate that SEM can efficiently defend current best synonym substitution
based adversarial attacks with almost no decay on the accuracy for benign
examples. Besides, to better evaluate SEM, we also propose a strong attack
method called Improved Genetic Algorithm (IGA) that adopts the genetic
metaheuristic against synonyms substitution based attacks. Compared with
existing genetic based adversarial attack, the proposed IGA can achieve higher
attack success rate at the same time maintain the transferability of
adversarial examples.
Source: arXiv (Cornell University)
Authors: Xiaosen Wang, Hao Jin, Kun He
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2972619028