| Challenge: | Recent methods for generating NLP adversarial examples involve combinatorial search and expensive sentence encoders for constraining the generated instances. |
| Approach: | They propose to use vanilla adversarial training to train NLP models using a word substitution attack optimized for vanilla adversary training. |
| Outcome: | The proposed approach improves model performance and standard accuracy and can defend against other types of word substitution attacks. |
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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. |
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. |
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Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks (2022.findings-naacl)
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| Challenge: | Existing methods to improve model robustness against word substitution-based adversarial attacks are too slow to generate adversarials on the fly. |
| Approach: | They propose an approach to improve the robustness of BERT models against word substitution-based adversarial attacks by leveraging adversarials for self-supervised contrastive learning. |
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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. |
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Deep Adversarial Learning for NLP (N19-5)
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| Challenge: | Adversarial learning is a game-theoretic learning paradigm that has achieved huge successes in the field of Computer Vision recently. |
| Approach: | This tutorial introduces the foundations of deep adversarial learning and some practical problems and solutions in NLP. |
| Outcome: | This tutorial introduces the foundations of deep adversarial learning and some practical problems and solutions in NLP. |
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. |
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TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP (2020.emnlp-demos)
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| 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. |
BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks (2021.findings-acl)
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| Challenge: | adversarial attacks expose important blind spots of deep learning systems, authors show . word and sentence-level attacks tend to be more difficult to defend via spelling correction modules . character-level attack scenarios often involve finding semantic paraphrases of input . |
| Approach: | They propose a model that probabilistically combines context-independent word level information with context-dependent information from BERT's masked language modeling to combat low-level orthographic attacks. |
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Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble (2021.acl-long)
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| Challenge: | Recent studies show vulnerability of deep neural networks to adversarial examples that intentionally fool the networks. |
| Approach: | They propose a method for training a robust model to defense synonym substitution-based attacks by sampling embedding vectors for each word in an input sentence and augmenting them with the training data. |
| Outcome: | The proposed method outperforms other proposed defense methods by a significant margin across different network architectures and multiple data sets. |
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)
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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials. |
| Approach: | They propose a flood method which aims at better generalization and a criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch. |
| Outcome: | The proposed method improves BERT’s resistance to textual adversarial attacks by a large margin and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks. |