Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification (D19-1)
Copied to clipboard
| 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. |
Similar Papers
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 . |
Generative Adversarial Training with Perturbed Token Detection for Model Robustness (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing adversarial training methods use discrete tokens to deceive models . current approaches use embeddings, whereas actual text-based training uses discrete text tokens. |
| Approach: | They propose a framework that integrates gradient-based learning, adversarial example generation and perturbed token detection to enhance adversariarial robustness. |
| Outcome: | The proposed framework surpasses the state-of-the-art results of ChatGPT by 10% in average accuracy. |
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent work has shown that models can be easily fooled by intentionally designed adversarial examples. |
| Approach: | They propose two efficient approaches for generating adversarial perturbations on embeddings and propose two new approaches to help model learn adversarials more efficiently. |
| Outcome: | The proposed approaches outperform strong baselines on various text classification datasets and the model's performance drops less under adversarial attack. |
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks (2022.acl-long)
Copied to clipboard
| 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. |
Synonym-unaware Fast Adversarial Training against Textual Adversarial Attacks (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing adversarial defense methods rely on predetermined linguistic knowledge and assume that attackers’ synonym candidates are known, which is often unrealistic. |
| Approach: | They propose a Fast Adversarial Training method that leverages single-step perturbation generation and effective perturbation initialization to improve model robustness without requiring synonym awareness. |
| Outcome: | Experiments show that the proposed method outperforms existing models under character-level and word-level attacks while still maintaining the correct syntax. |
Weight Perturbation as Defense against Adversarial Word Substitutions (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existence and pervasiveness of textual adversarial examples have raised serious concerns to security-critical applications. |
| Approach: | They propose to perform weight perturbations in the parameter space rather than the input feature space to improve adversarial robustness of NLP models. |
| Outcome: | The proposed method improves adversarial robustness of models by performing weight perturbations in the parameter space rather than the input feature space. |
Generating Textual Adversaries with Minimal Perturbation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing word-level adversarial approaches for textual data have various limitations due to the large search space consisting of combinations of candidate words. |
| Approach: | They propose a novel attack strategy to find adversarial texts with high similarity to original texts without perturbation. |
| Outcome: | The proposed approach achieves higher success rates and lower perturbation rates in four benchmark datasets compared with state-of-the-art approaches. |
Adversarial Text Normalization (2022.naacl-industry)
Copied to clipboard
| Challenge: | Text-based adversarial attacks are becoming more commonplace and accessible to general internet users. |
| Approach: | They propose a method that restores baseline performance on attacked content with low computational overhead. |
| Outcome: | The proposed method restores baseline performance on attacked content with low computational overhead. |
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution (2021.emnlp-main)
Copied to clipboard
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. |
| Approach: | They propose to compare different defense methods under representative adversarial attacks . they propose a method that improves the robustness of neural text classifiers against such attacks a . |
| Outcome: | The proposed method improves robustness of neural text classifiers against such attacks by a significant margin. |
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP (2020.emnlp-demos)
Copied to clipboard
| Challenge: | TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets. |
| Approach: | They introduce a Python framework for adversarial attacks, data augmentation, and adversarially training in NLP. |
| Outcome: | This paper introduces a Python framework for adversarial attacks, data augmentation, and adversarially training in NLP. |