| Challenge: | Recent work in natural language processing generates adversarial examples using white-box access . a neural network can learn to emulate the behavior of a white- box attack and generalize well to new examples. |
| Approach: | They propose an adversarial training approach that assumes white-box access to an attacker's model and optimizes the input directly against it. |
| Outcome: | The proposed approach reduces example generation time by 19x-39x and exposes the Google Perspective API vulnerability. |
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HotFlip: White-Box Adversarial Examples for Text Classification (P18-2)
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| Challenge: | Existing methods to create adversarial examples without explicit knowledge of model parameters are not effective. |
| Approach: | They propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier by an atomic flip operation. |
| Outcome: | The proposed method can be adapted to attack a word-level classifier with a few constraints. |
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. |
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. |
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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. |
| Approach: | They propose a grey-box adversarial attack and defence framework for sentiment classification . they show that the framework produces an improved classifier that is robust in defending . |
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NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries? (2022.findings-emnlp)
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| Challenge: | Existing work has explored adversarial example generation for natural language understanding tasks, but these examples are often unrealistic and diverge from the real-world data distributions. |
| Approach: | They propose a framework for adversarial example generation that is effective at fooling a given classifier and a generative model based on the key tokens from the first stage. |
| Outcome: | The proposed framework generalizes across domains and offers insights for future research on improving robustness of neural text classification models. |
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples (2020.acl-main)
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| Challenge: | Previously studies focused on semantic tasks such as sentiment analysis, question answering and reading comprehension. |
| Approach: | They propose two approaches to study where and how adversarial examples exist in dependency parsing . they use a state-of-the-art parser to find adversarials in existing texts . |
| Outcome: | The proposed approaches show that adversarial examples exist in dependency parsing . they show that up to 77% of input examples admit adversarials . |
Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation (2025.findings-naacl)
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| Challenge: | Existing adversarial approaches to verify the vulnerability of language models require numerous queries and the information on the target model. |
| Approach: | They propose a method that generates adversarial examples without accessing the target model and uses controlled generation techniques to generate adversarials. |
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Generating Natural Language Adversarial Examples (D18-1)
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| Challenge: | Recent research has shown that deep neural networks are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. |
| Approach: | They propose to generate adversarial examples that fool well-trained sentiment analysis and textual entailment models by using a black-box population-based optimization algorithm. |
| Outcome: | The proposed model is able to fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. |
XAI-Attack: Utilizing Explainable AI to Find Incorrectly Learned Patterns for Black-Box Adversarial Example Creation (2024.lrec-main)
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| Challenge: | Adversarial examples can be used to trick machine learning models into making erroneous predictions, causing poorer insights and lower confidence in the information gathered. |
| Approach: | They propose a textual adversarial example method that identifies falsely learned word indicators by leveraging explainable AI methods as importance functions on incorrectly predicted instances. |
| Outcome: | The proposed method outperforms existing examples and training methods and shows baseline improvements of up to 23 percentage points on adversarial tasks. |
Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation (2021.findings-acl)
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| Challenge: | Open-domain neural dialogue models have achieved high performance in response ranking and evaluation tasks. |
| Approach: | They propose methods for automatically creating adversarial negative training data . they use mask-and-fill and keyword-guided approaches to generate negative examples . |
| Outcome: | The proposed approaches outperform baseline models in providing informative negative examples for training dialogue systems. |