Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model
Name
1909.07873.pdf
Description
Accepted version
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635.44 KB
Format
Adobe PDF
Checksum (MD5)
0d6d69cd645743748c0dd74e98ed1cc1
Author(s)
Vijayaraghavan, P
Roy, D
Date Issued
2020
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer International Publishing
Citation
Vijayaraghavan, P and Roy, D. 2020. "Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11907 LNAI.
Version
Author's final manuscript
Abstract
© Springer Nature Switzerland AG 2020. Recently, generating adversarial examples has become an important means of measuring robustness of a deep learning model. Adversarial examples help us identify the susceptibilities of the model and further counter those vulnerabilities by applying adversarial training techniques. In natural language domain, small perturbations in the form of misspellings or paraphrases can drastically change the semantics of the text. We propose a reinforcement learning based approach towards generating adversarial examples in black-box settings. We demonstrate that our method is able to fool well-trained models for (a) IMDB sentiment classification task and (b) AG’s news corpus news categorization task with significantly high success rates. We find that the adversarial examples generated are semantics-preserving perturbations to the original text.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-030-46147-8_43
