| Challenge: | Current methods for building adversarial attackers for NLP are inefficient as the gradient is discarded. |
| Approach: | They propose an adversarial attacker which performs Metropolis-Hastings sampling with the guidance of gradients to solve these problems. |
| Outcome: | The proposed algorithm outperforms the baseline model on attacking capability on IMDB and SNLI. |
Similar Papers
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. |
| Approach: | They propose a unified definition of what constitutes a successful adversarial example . they propose four categories of constraints that are used to define adversarials . |
| Outcome: | The proposed framework is based on the outputs of two state-of-the-art synonym substitution attacks. |
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. |
A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples (2020.coling-main)
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| Challenge: | Existing techniques to generate adversarial examples for natural language are limited . previous research on adversarials focused on images, but this is not the case with natural language. |
| Approach: | They propose a geometry-inspired attack for generating natural language adversarial examples . they use deep neural networks to iteratively approximate the decision boundary . |
| Outcome: | The proposed attack fools natural language models with high success rates while replacing a few words. |
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. |
| Outcome: | The proposed method outperforms state-of-the-art methods in success rate and perturb percentage while remaining fluent and semantically preserved. |
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. |
| Approach: | They propose a framework that learns to attack a structured prediction model with feedbacks from multiple reference models. |
| Outcome: | The proposed framework is able to attack state-of-the-art models and boost them with training . it is based on a sequence-to-sequence model with feedbacks from multiple reference models . |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
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| Challenge: | Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans. |
| Approach: | They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations. |
| Outcome: | The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling. |
Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models (2025.emnlp-main)
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| Challenge: | Large language models can be used to attack content filtering algorithms in social media platforms. |
| Approach: | They propose to generate adversarial examples to test the robustness of social media content filtering algorithms. |
| Outcome: | The proposed model outperforms existing models in the case of propaganda, false claims, rumours and hyperpartisan news. |
Vulnerabilities of Large Language Models to Adversarial Attacks (2024.acl-tutorials)
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| Challenge: | This tutorial focuses on the vulnerabilities of Large Language Models to adversarial attacks . the tutorial lays the foundation by explaining safety-aligned models and concepts in cybersecurity . |
| Approach: | This tutorial lays the foundation by explaining safety-aligned LLMs and concepts in cybersecurity. |
| Outcome: | The tutorial lays the foundation by explaining safety-aligned models and concepts in cybersecurity. |
Identifying Human Strategies for Generating Word-Level Adversarial Examples (2022.findings-emnlp)
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| Challenge: | a recent study shows that word-level adversarial examples are more natural and grammatical correct than automated attacks. |
| Approach: | They analyze how humans generate word-level adversarial examples against fine-tuned Transformer models that preserve naturalness and grammatical correctness. |
| Outcome: | The authors show that humans generate adversarial examples much more effortlessly than automated attacks. |
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework (2023.findings-acl)
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| Challenge: | Existing methods for adversarial samples are poorly applied in computer vision . however, textual adversarials are still vulnerable to small perturbations . |
| Approach: | They propose a framework to extend existing adversarial attack methods to textual adversarials by adding optimized perturbations to embedding layer and amplifying them in forward propagation process. |
| Outcome: | The proposed framework achieves better performance even using proxy gradient information and produces more fluent and grammatical adversarial samples compared to baseline methods. |