Challenge: Existing text attack methods are designed for English text, but robust implementation of Chinese text is understudied.
Approach: They propose an adaptive immune-based sound-shape code algorithm for Chinese text attacks . they leverage the Sound-Shape Code to generate natural substitutions .
Outcome: The proposed algorithm produces high-quality Chinese adversarial examples . it can reduce duplication of population and improve search ability .

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Enhance Robustness of Language Models against Variation Attack through Graph Integration (2024.lrec-main)

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Challenge: Pre-trained language models (PLMs) are used in many NLP applications but their vulnerability to adversarial attacks can lead to false or misleading information being distributed.
Approach: They propose a method to incorporate a Chinese character variation graph into pre-trained language models to increase their robustness against character variation attacks in Chinese content.
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Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution (2021.emnlp-main)

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Challenge: Existing methods to defend against adversarial word-substitution attacks have not been evaluated or compared in a systematic manner.
Approach: They propose to compare different defense methods under representative adversarial attacks . they propose a method that improves the robustness of neural text classifiers against such attacks a .
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Universal Adversarial Attacks with Natural Triggers for Text Classification (2021.naacl-main)

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Challenge: Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by classifier.
Approach: They propose a gradient-based search that aims to maximize the downstream classifier’s prediction loss by using an adversarially regularized autoencoder to generate triggers and propose heuristics to spot such attacks.
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Adversarial Text Normalization (2022.naacl-industry)

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Challenge: Text-based adversarial attacks are becoming more commonplace and accessible to general internet users.
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Outcome: The proposed method restores baseline performance on attacked content with low computational overhead.
Don’t be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks (2024.findings-naacl)

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Challenge: Offensive language detection is important for filtering out abusive expressions, authors argue . authors propose user-intended adversarial attacks that insert special symbols or leverage distinctive features of the Korean language.
Approach: They propose user-intended adversarial attacks that insert special symbols or leverage the distinctive features of the Korean language.
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Reevaluating Adversarial Examples in Natural Language (2020.findings-emnlp)

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Challenge: State-of-the-art adversarial examples lack a common definition of what constitutes success . human surveys show that to preserve semantics, we need to increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences.
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TinyAttack: Exploring Stylistic Vulnerabilities in Large Language Models (2026.findings-acl)

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Challenge: Existing research on robustness of large language models has focused on text-based perturbations and the use of invisible characters and homoglyphs.
Approach: They propose a framework to exploit weaknesses in Large Language Models (LLMs) by changing their stylistic structure using Unicode.
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Grey-box Adversarial Attack And Defence For Sentiment Classification (2021.naacl-main)

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Challenge: Recent advances in deep neural networks have created applications for a range of different domains.
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Outcome: The proposed framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods.
Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks (2025.coling-main)

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Challenge: Recent advances in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks.
Approach: They propose a benchmark for textual adversarial defence that evaluates state-of-the-art defence mechanisms across diverse datasets, models, and tasks.
Outcome: The proposed benchmark incorporates a wide range of datasets and evaluates state-of-the-art defence mechanisms.
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification (2021.emnlp-main)

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Challenge: Recent work has raised the question of whether valid adversarial inputs are feasible.
Approach: They analyze how human-generated adversarial examples compare to the best algorithms . they use crowdsourcing to modify words in an input text with immediate feedback .
Outcome: The proposed algorithms are not more efficient than the best to generate natural-reading, sentiment-preserving examples.

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