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2024 | OriginalPaper | Chapter

Adversarial Text Purification: A Large Language Model Approach for Defense

Authors : Raha Moraffah, Shubh Khandelwal, Amrita Bhattacharjee, Huan Liu

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer Nature Singapore

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Abstract

Adversarial purification is a defense mechanism for safeguarding classifiers against adversarial attacks without knowing the type of attacks or training of the classifier. These techniques characterize and eliminate adversarial perturbations from the attacked inputs, aiming to restore purified samples that retain similarity to the initially attacked ones and are correctly classified by the classifier. Due to the inherent challenges associated with characterizing noise perturbations for discrete inputs, adversarial text purification has been relatively unexplored. In this paper, we investigate the effectiveness of adversarial purification methods in defending text classifiers. We propose a novel adversarial text purification that harnesses the generative capabilities of Large Language Models (LLMs) to purify adversarial text without the need to explicitly characterize the discrete noise perturbations. We utilize prompt engineering to exploit LLMs for recovering the purified samples for given adversarial examples such that they are semantically similar and correctly classified. Our proposed method demonstrates remarkable performance over various classifiers, improving their accuracy under the attack by over 65% on average.

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Metadata
Title
Adversarial Text Purification: A Large Language Model Approach for Defense
Authors
Raha Moraffah
Shubh Khandelwal
Amrita Bhattacharjee
Huan Liu
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-64-8

Electronic ISBN: 978-981-9722-62-4

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-2262-4_6

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