Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP (2022.emnlp-main)
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| Challenge: | Textual adversarial samples are often misrepresented in research on security, evaluation, explainability, and data augmentation. |
| Approach: | They propose to use adversarial samples to evaluate their methods on security tasks to demonstrate the real-world concerns rather than developing impractical methods. |
| Outcome: | The proposed method has higher practical value than the current benchmark. |
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| Challenge: | Existing text adversarial attacks are impractical in real-world scenarios where humans are involved. |
| Approach: | They have surveyed 378 human participants about the perceptibility of text adversarial examples produced by state-of-the-art methods. |
| Outcome: | The proposed methods ignore the property of imperceptibility or study it under limited conditions. |
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. |
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Deep Adversarial Learning for NLP (N19-5)
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| Challenge: | Adversarial learning is a game-theoretic learning paradigm that has achieved huge successes in the field of Computer Vision recently. |
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Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
| Approach: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs . |
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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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Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks (2021.emnlp-main)
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| Challenge: | Recent research has focused on adversarial text attacks on neural networks for natural language processing. |
| Approach: | They implement an algorithm inspired by zeroth order optimization-based attacks and compare it with benchmark results in TextAttack. |
| Outcome: | The proposed algorithm outperforms other black-box adversarial text attacks. |
BERT-ATTACK: Adversarial Attack Against BERT Using BERT (2020.emnlp-main)
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| Challenge: | Current approaches to generate adversarial samples for discrete data are heuristic replacement strategies that are difficult to implement in continuous data. |
| Approach: | They propose a method to generate adversarial samples using pre-trained masked language models using BERT. |
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Adversarial Attack and Defense of Structured Prediction Models (2020.emnlp-main)
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| Challenge: | Existing approaches to building effective adversarial attackers focus on classification problems. |
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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. |
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Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense (2022.findings-acl)
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| Challenge: | ANTHRO extracts over 600K human-written text perturbations and leverages them for realistic adversarial attacks. |
| Approach: | They propose an adversarial text manipulation algorithm that inductively extracts over 600K human-written text perturbations and leverages them for realistic adversarials. |
| Outcome: | The proposed algorithm outperforms the TextBugger baseline with an increase of 50% and 40% in terms of semantic preservation and stealthiness when evaluated by layperson and professional human workers. |