Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers

• URL: http://arxiv.org/abs/2401.17196v1
• Date: Tue, 30 Jan 2024 17:30:44 GMT
• Title: Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers
• Authors: Lei Xu, Sarah Alnegheimish, Laure Berti-Equille, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
• Abstract summary: A significant portion of adversarial examples generated by existing methods change only one word. This single-word perturbation vulnerability represents a significant weakness in classifiers. We present the SP-Attack, designed to exploit the single-word perturbation vulnerability, achieving a higher attack success rate. We also propose SP-Defense, which aims to improve rho by applying data augmentation in learning.
• Score: 12.167426402230229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In text classification, creating an adversarial example means subtly perturbing a few words in a sentence without changing its meaning, causing it to be misclassified by a classifier. A concerning observation is that a significant portion of adversarial examples generated by existing methods change only one word. This single-word perturbation vulnerability represents a significant weakness in classifiers, which malicious users can exploit to efficiently create a multitude of adversarial examples. This paper studies this problem and makes the following key contributions: (1) We introduce a novel metric \r{ho} to quantitatively assess a classifier's robustness against single-word perturbation. (2) We present the SP-Attack, designed to exploit the single-word perturbation vulnerability, achieving a higher attack success rate, better preserving sentence meaning, while reducing computation costs compared to state-of-the-art adversarial methods. (3) We propose SP-Defense, which aims to improve \r{ho} by applying data augmentation in learning. Experimental results on 4 datasets and BERT and distilBERT classifiers show that SP-Defense improves \r{ho} by 14.6% and 13.9% and decreases the attack success rate of SP-Attack by 30.4% and 21.2% on two classifiers respectively, and decreases the attack success rate of existing attack methods that involve multiple-word perturbations.

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