DA3: A Distribution-Aware Adversarial Attack against Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent attacks have shown that adversarial examples have a different data distribution than the original examples, reducing their effectiveness under detection methods. |
| Approach: | They propose a distribution-aware adversarial attack method that considers the distribution shifts of adversarials to improve attacks’ effectiveness under detection methods. |
| Outcome: | The proposed method improves the effectiveness of adversarial examples under detection methods and integrates both ASR and detectability. |
Similar Papers
BERT-ATTACK: Adversarial Attack Against BERT Using BERT (2020.emnlp-main)
Copied to clipboard
| 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. |
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack (2020.emnlp-main)
Copied to clipboard
| 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. |
Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models (2025.emnlp-main)
Copied to clipboard
| 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. |
From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks (2020.aacl-main)
Copied to clipboard
| Challenge: | Adversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. |
| Approach: | They propose to use a dataset to test the robustness of future NLP models to identify low-level adversarial attacks that are less realistic in typical applications such as social media. |
| Outcome: | The proposed dataset provides a benchmark for testing robustness of future more human-like NLP models. |
Adversarial Attack and Defense of Structured Prediction Models (2020.emnlp-main)
Copied to clipboard
| 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 . |
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers (2023.findings-emnlp)
Copied to clipboard
| 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. |
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! (2021.naacl-main)
Copied to clipboard
| Challenge: | Pretrained language models are used for natural language processing (NLP) but when they are deployed as a service, they can suffer from different attacks . |
| Approach: | They propose two defence strategies to protect the target model from adversarial attacks . they show that model extraction and adversarially transferable attacks can be effective . |
| Outcome: | The extracted model can lead to highly transferable adversarial attacks against the target model. |
Grey-box Adversarial Attack And Defence For Sentiment Classification (2021.naacl-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods. |
Detection of Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation (2022.findings-acl)
Copied to clipboard
| Challenge: | Word-level adversarial attacks have shown success in NLP, decreasing performance of transformer-based models with smaller perturbation rate. |
| Approach: | They propose a dataset for four popular attack methods on four datasets and four models to encourage further research in this field. |
| Outcome: | The proposed baseline has the highest auc on 29 out of 30 dataset-attack-model combinations. |
No offence, Bert - I insult only humans! Multilingual sentence-level attack on toxicity detection networks (2023.findings-emnlp)
Copied to clipboard
| Challenge: | a new sentence-level attack on toxic detection models is shown to work on seven languages . toxicity detection systems are used to silence the voices of criticism, causing echo chambers . |
| Approach: | They propose a sentence-level attack that adds positive words to a hateful message . they show the attack works on seven languages from three different language families . |
| Outcome: | The proposed attack is shown to work on seven languages from three different language families. |