Papers with Adversarial attacks
Concealed Data Poisoning Attacks on NLP Models (2021.naacl-main)
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| Challenge: | In contrast, adversarial attacks can cause model errors by modifying inputs, such as the universal triggers attack. |
| Approach: | They propose a data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input. |
| Outcome: | The proposed attack can cause model errors by modifying inputs, but it can also cause extra human annotation. |
SeqAttack: On Adversarial Attacks for Named Entity Recognition (2021.emnlp-demo)
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| Challenge: | Named Entity Recognition (NER) is a task of recognizing named entities in a chunk of text. |
| Approach: | They investigate the portability of adversarial attacks from text classification to named entity recognition and the ability of adversary training to counteract these attacks. |
| Outcome: | The proposed framework and web application can be used to cherry pick adversarial examples and perform character-level and word-level attacks. |
From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks (2020.aacl-main)
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| 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. |
RMLM: A Flexible Defense Framework for Proactively Mitigating Word-level Adversarial Attacks (2023.acl-long)
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| Challenge: | Existing defenses focus on improving robustness of the victim model in training, but neglect to mitigate adversarial attacks during inference. |
| Approach: | They propose a framework that confuses attackers and corrects adversarial contexts . their framework helps improve the robustness of the victim model during inference . |
| Outcome: | The proposed framework improves the robustness of the victim model in training . it also corrects abnormal contexts in the representation level and filtering out examples . |
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations (2023.findings-acl)
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| Challenge: | Existing methods for detecting adversarial attacks need original/adversarial data. |
| Approach: | They propose to detect adversarial text by voting on hard labels from predictions of transformations using a model that uses original/adversarial data. |
| Outcome: | The proposed method detects adversarial text across various state-of-the-art attacks, models, and datasets without training. |
Towards Imperceptible Document Manipulations against Neural Ranking Models (2023.findings-acl)
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| Challenge: | Current approaches to detect vulnerabilities in neural ranking models often introduce noticeable errors and require a well-imitated surrogate NRM to guarantee the attack effect. |
| Approach: | They propose a framework called Imperceptible DocumEnt Manipulation to produce adversarial documents that are less noticeable to both algorithms and humans. |
| Outcome: | The proposed framework outperforms strong baselines while maintaining fluency and correctness of the target documents. |
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification (D19-1)
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| Challenge: | Existing studies on adversarial attacks on deep learning models focus on generation of adversarials and defense against adversarial attacks. |
| Approach: | They propose a framework to identify and adjust malicious perturbations and block adversarial attacks for machine learning models. |
| Outcome: | The proposed framework outperforms baseline methods in blocking adversarial attacks for text classification models. |
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. |
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks (2022.acl-long)
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| Challenge: | Existing methods to detect adversarial text inputs are limited in performance and are not detectable via spell checkers. |
| Approach: | They propose a model-agnostic detector of adversarial text examples that detects patterns in the logits of the target classifier when perturbing the input text. |
| Outcome: | The proposed detector improves the state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks. |
Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)
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| Challenge: | Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms. |
| Approach: | They propose a novel adversarial adversarialist model that incorporates word substitution and particle swarm optimization to solve two problems separately. |
| Outcome: | The proposed model achieves much higher success rates and crafts more high-quality adversarial examples as compared to baseline methods. |
NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic (2023.acl-long)
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| Challenge: | Despite the recent advances in distributed representation and neural networks, it remains an open question whether the models perform real reasoning to reach their conclusions or rely on spurious correlations. |
| Approach: | They propose to use logic formalism to perform systematic attacks centring around natural logic to generate better adversarial examples with fewer visits to the victim models. |
| Outcome: | The proposed framework generates better adversarial examples with fewer visits to the victim models. |
OSCR-Attack: One-Shot Character Level Attacks through Self-Optimizing Continuous Relaxation (2026.findings-acl)
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Lingyi Kong, Zhuo Liu, Zhanghao Hu, Qilong Qiu, Yutao Yang, Jingjing Xue, Zheng Wang, Lin Gui, Feiping Nie
| Challenge: | Character-level adversarial attacks preserve semantics but are costly and inefficient . generative LLMs are gaining popularity due to their uncertainty and vulnerability to textual adversarials . |
| Approach: | They propose an end-to-end framework that transforms discrete choices into continuous representations and a conflict resolution strategy that maps them back into discrete insertion operations. |
| Outcome: | The proposed framework improves ASR by 21.45% points and accelerates the attack by 3.66 times compared to baselines. |