| Challenge: | Recent studies have pointed out the vulnerability of deep learning models to adversarial attacks. |
| Approach: | They propose a black-box attack method that adapts the idea of Occlusion and Language Models to the current state of the art attack methods. |
| Outcome: | The proposed method outperforms existing methods on several text classification tasks. |
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Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution (2021.emnlp-main)
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Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh
| 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 . |
| Outcome: | The proposed method improves robustness of neural text classifiers against such attacks by a significant margin. |
Adversarial Text Generation by Search and Learning (2023.findings-emnlp)
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Guoyi Li, Bingkang Shi, Zongzhen Liu, Dehan Kong, Yulei Wu, Xiaodan Zhang, Longtao Huang, Honglei Lyu
| Challenge: | Existing text generation methods only use heuristic replacement strategies or language models to generate replacement words at the word level. |
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| Outcome: | The proposed methods are significantly superior to the most advanced methods in terms of attack efficiency and adversarial text quality. |
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack (2024.lrec-main)
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| Challenge: | Despite the development of large language models, there are still significant challenges in detecting whether text is generated by a machine. |
| Approach: | They propose a framework for a broader class of adversarial attacks to perform minor perturbations in machine-generated content to evade detection. |
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BAE: BERT-based Adversarial Examples for Text Classification (2020.emnlp-main)
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| Challenge: | Recent studies have exposed the vulnerability of text classification models to adversarial examples . perturbed versions of the original text are indiscernible by humans and misclassified by the model . |
| Approach: | They propose a black box attack for generating adversarial examples using contextual perturbations from a BERT-masked language model. |
| Outcome: | The proposed attack produces examples with improved grammaticality and semantic coherence compared to previous work. |
Query-Efficient Textual Adversarial Example Generation for Black-Box Attacks (2024.naacl-long)
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| Challenge: | Existing black-box attacks require thousands of queries on the target model, making them expensive in real-world applications. |
| Approach: | They propose a new approach that guides word substitutions using prior knowledge from the training set to improve the attack efficiency. |
| Outcome: | The proposed approach reduces query-free attack and guided search attacks by a factor of 10 500 . it improves transferability and generalization by the ensemble of the ABPens in NLP . |
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. |
On Adversarial Examples for Character-Level Neural Machine Translation (C18-1)
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| Challenge: | Using adversarial examples to measure robustness of deep learning models has become a standard procedure due to the difficulty of creating white-box adversarials for discrete text input. |
| Approach: | They propose two novel attacks which aim to remove or change a word in a translation, rather than simply break the NMT. |
| Outcome: | The proposed attacks are significantly stronger than their black-box counterparts in different attack scenarios, showing more serious vulnerabilities than previously known. |
Adversarial Reprogramming of Text Classification Neural Networks (D19-1)
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| Challenge: | Recent studies have shown that adversarial examples can cause a machine learning model to misclassify a sample from the classifier's input domain. |
| Approach: | They propose a context-based vocabulary remapping method that performs a computationally inexpensive input transformation to reprogram a victim classification model for a new set of sequences. |
| Outcome: | The proposed method performs a cost-effective input transformation to reprogram a model for a new set of sequences without altering the network architecture or parameters. |
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack (2020.emnlp-main)
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| Challenge: | Existing adversarial examples can induce arbitrary errors to the target models, but they can be exploited to estimate robustness of NLP models. |
| Approach: | They propose a target-controllable adversarial attack framework T3 to handle adversarials . they use tree-based decoders to regularize the syntactic correctness of generated text . |
| Outcome: | The proposed framework can be used to estimate the robustness of NLP models. |
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers (2023.findings-emnlp)
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| Challenge: | Backdoor attacks manipulate model predictions by inserting malicious "poison" instances that contain a specific pattern or "trigger." |
| Approach: | They propose an attack that inserts style-based triggers into training and test data by using a poison selection technique to improve the effectiveness of both LLMBkd and existing backdoor attacks. |
| Outcome: | The proposed attack achieves high success rates across a wide range of styles with little effort and no model training. |