mlsanityData to 7 Oct 2026

Adversarial Machine Learning in Text Analysis and Generation

arXiv 2101.08675 ↗First posted 14 Jan 2021cs.LG cs.CRcited by 2

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Abstract (arXiv)

The research field of adversarial machine learning witnessed a significant interest in the last few years. A machine learner or model is secure if it can deliver main objectives with acceptable accuracy, efficiency, etc. while at the same time, it can resist different types and/or attempts of adversarial attacks. This paper focuses on studying aspects and research trends in adversarial machine learning specifically in text analysis and generation. The paper summarizes main research trends in the field such as GAN algorithms, models, types of attacks, and defense against those attacks.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 133 of its 194 references that are in the map. First posted on arXiv 14 Jan 2021.

Builds on

133 of 194 references.

References · 133

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38 more
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50 most cited of 133 All 133 (JSON)

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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

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