Related papers: On the Transferability of Adversarial Attacksagainst Neural Text Classifier

On the Transferability of Adversarial Attacksagainst Neural Text Classifier

URL: http://arxiv.org/abs/2011.08558v3
Date: Wed, 22 Sep 2021 02:35:08 GMT
Title: On the Transferability of Adversarial Attacksagainst Neural Text Classifier
Authors: Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-wei Chang
Abstract summary: We investigate the transferability of adversarial examples for text classification models. We propose a genetic algorithm to find an ensemble of models that can induce adversarial examples to fool almost all existing models. We derive word replacement rules that can be used for model diagnostics from these adversarial examples.
Score: 121.6758865857686
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Deep neural networks are vulnerable to adversarial attacks, where a small perturbation to an input alters the model prediction. In many cases, malicious inputs intentionally crafted for one model can fool another model. In this paper, we present the first study to systematically investigate the transferability of adversarial examples for text classification models and explore how various factors, including network architecture, tokenization scheme, word embedding, and model capacity, affect the transferability of adversarial examples. Based on these studies, we propose a genetic algorithm to find an ensemble of models that can be used to induce adversarial examples to fool almost all existing models. Such adversarial examples reflect the defects of the learning process and the data bias in the training set. Finally, we derive word replacement rules that can be used for model diagnostics from these adversarial examples.

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