Model Robustness with Text Classification: Semantic-preserving adversarial attacks

• URL: http://arxiv.org/abs/2008.05536v2
• Date: Fri, 14 Aug 2020 01:05:09 GMT
• Title: Model Robustness with Text Classification: Semantic-preserving adversarial attacks
• Authors: Rahul Singh, Tarun Joshi, Vijayan N. Nair, and Agus Sudjianto
• Abstract summary: We propose algorithms to create adversarial attacks to assess model robustness in text classification problems. The attacks cause significant number of flips in white-box setting and same rule based can be used in black-box setting.
• Score: 12.31604391452686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose algorithms to create adversarial attacks to assess model robustness in text classification problems. They can be used to create white box attacks and black box attacks while at the same time preserving the semantics and syntax of the original text. The attacks cause significant number of flips in white-box setting and same rule based can be used in black-box setting. In a black-box setting, the attacks created are able to reverse decisions of transformer based architectures.

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