| Challenge: | Recent studies have shown that adversarial examples can cause a machine learning model to misclassify a sample from the classifier's input domain. |
| Approach: | They propose a context-based vocabulary remapping method that performs a computationally inexpensive input transformation to reprogram a victim classification model for a new set of sequences. |
| Outcome: | The proposed method performs a cost-effective input transformation to reprogram a model for a new set of sequences without altering the network architecture or parameters. |
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WARP: Word-level Adversarial ReProgramming (2021.acl-long)
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| Challenge: | Existing approaches to transfer learning from pretrained language models are frozen and a task-specific head is trained on top of them. |
| Approach: | They propose an alternative approach that trains one or more task-specific layers on top of the language model. |
| Outcome: | The proposed approach outperforms existing methods on the GLUE leaderboard with just 32 training samples. |
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder (2021.findings-acl)
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| 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. |
Adv-OLM: Generating Textual Adversaries via OLM (2021.eacl-main)
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| Challenge: | Recent studies have pointed out the vulnerability of deep learning models to adversarial attacks. |
| Approach: | They propose a black-box attack method that adapts the idea of Occlusion and Language Models to the current state of the art attack methods. |
| Outcome: | The proposed method outperforms existing methods on several text classification tasks. |
A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)
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| Challenge: | Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems. |
| Approach: | They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric. |
| Outcome: | The proposed paradigm produces stable attacks with meaning-preserving adversarial examples. |
BAE: BERT-based Adversarial Examples for Text Classification (2020.emnlp-main)
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| Challenge: | Recent studies have exposed the vulnerability of text classification models to adversarial examples . perturbed versions of the original text are indiscernible by humans and misclassified by the model . |
| Approach: | They propose a black box attack for generating adversarial examples using contextual perturbations from a BERT-masked language model. |
| Outcome: | The proposed attack produces examples with improved grammaticality and semantic coherence compared to previous work. |
XVD: Cross-Vocabulary Differentiable Training for Generative Adversarial Attacks (2024.lrec-main)
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| Challenge: | Existing approaches to create adversarial examples using tokens are not sufficient to ensure other desirable properties such as similarity to non-adversarial examples, linguistic fluency, and so forth. |
| Approach: | They propose a method which leverages a set of pretrained language models to promote similarity to non-adversarial examples, linguistic fluency, and so forth. |
| Outcome: | The proposed approach outperforms existing methods and is competitive with token-based approaches. |
Adversarial Text Generation by Search and Learning (2023.findings-emnlp)
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Guoyi Li, Bingkang Shi, Zongzhen Liu, Dehan Kong, Yulei Wu, Xiaodan Zhang, Longtao Huang, Honglei Lyu
| Challenge: | Existing text generation methods only use heuristic replacement strategies or language models to generate replacement words at the word level. |
| Approach: | They propose a search and learning framework for Adversarial Text Generation by Search and Learning to evaluate the robustness of natural language processing models. |
| Outcome: | The proposed methods are significantly superior to the most advanced methods in terms of attack efficiency and adversarial text quality. |
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. |
| 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. |
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher (2022.acl-long)
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| Challenge: | Existing methods to defend textual neural network models against adversarial attacks often require retraining and retrain . e.g., BERT, RoBERTa require great time and computation resources. |
| Approach: | They propose an algorithm that modifies and re-trains only the last layer of a textual NN and transforms it into a stochastic weighted ensemble of multi-expert prediction heads. |
| Outcome: | The proposed algorithm outperforms existing models against black-box attacks by 15%–70% . the proposed algorithm is based on a novel algorithm from software engineering . |
HotFlip: White-Box Adversarial Examples for Text Classification (P18-2)
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| Challenge: | Existing methods to create adversarial examples without explicit knowledge of model parameters are not effective. |
| Approach: | They propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier by an atomic flip operation. |
| Outcome: | The proposed method can be adapted to attack a word-level classifier with a few constraints. |