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        <title>Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models</title>
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            <title>Proceedings of the 18th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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            <namePart type="family">Devi</namePart>
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            <namePart type="given">Pushpak</namePart>
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    <abstract>Attacks on deep learning models are often difficult to identify and therefore are difficult to protect against. This problem is exacerbated by the use of public datasets that typically are not manually inspected before use. In this paper, we offer a solution to this vulnerability by using, during testing, random perturbations such as spelling correction if necessary, substitution by random synonym, or simply drop-ping the word. These perturbations are applied to random words in random sentences to defend NLP models against adversarial attacks. Our Random Perturbations Defense andIncreased Randomness Defense methods are successful in returning attacked models to similar accuracy of models before attacks. The original accuracy of the model used in this work is 80% for sentiment classification. After undergoing attacks, the accuracy drops to an accuracy between 0% and 44%. After applying our defense methods, the accuracy of the model is returned to the original accuracy within statistical significance.</abstract>
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%0 Conference Proceedings
%T Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models
%A Swenor, Abigail
%A Kalita, Jugal
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F swenor-kalita-2021-using
%X Attacks on deep learning models are often difficult to identify and therefore are difficult to protect against. This problem is exacerbated by the use of public datasets that typically are not manually inspected before use. In this paper, we offer a solution to this vulnerability by using, during testing, random perturbations such as spelling correction if necessary, substitution by random synonym, or simply drop-ping the word. These perturbations are applied to random words in random sentences to defend NLP models against adversarial attacks. Our Random Perturbations Defense andIncreased Randomness Defense methods are successful in returning attacked models to similar accuracy of models before attacks. The original accuracy of the model used in this work is 80% for sentiment classification. After undergoing attacks, the accuracy drops to an accuracy between 0% and 44%. After applying our defense methods, the accuracy of the model is returned to the original accuracy within statistical significance.
%U https://aclanthology.org/2021.icon-main.63
%P 519-528

Markdown (Informal)

[Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models](https://aclanthology.org/2021.icon-main.63) (Swenor & Kalita, ICON 2021)

• Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models (Swenor & Kalita, ICON 2021)

ACL

• Abigail Swenor and Jugal Kalita. 2021. Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 519–528, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).