SHARP: Search-Based Adversarial Attack for Structured Prediction (2022.findings-naacl)
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| Challenge: | SHARP is a new attack method for structured prediction models that solves several challenges. |
| Approach: | They propose a black-box adversarial attack method that uses a search-based optimization problem to attack adversarials. |
| Outcome: | The proposed method performs more potent attack than pioneer arts on two structured prediction tasks. |
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| Challenge: | Existing approaches to building effective adversarial attackers focus on classification problems. |
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Guoyi Li, Bingkang Shi, Zongzhen Liu, Dehan Kong, Yulei Wu, Xiaodan Zhang, Longtao Huang, Honglei Lyu
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| Challenge: | Existing black-box adversarial attacks on pre-trained models generate adversarials with greedy search. |
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| Challenge: | Recent advances in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks. |
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| Challenge: | Recent studies have highlighted the lack of adversarial robustness in pre-trained models. |
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| Challenge: | Existing methods for obtaining adversarial examples are difficult with text data. |
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| Challenge: | Existing sentences use either no feedback or only the class labels for sentence-level attacks. |
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Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks (2021.emnlp-main)
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| Challenge: | Recent research has focused on adversarial text attacks on neural networks for natural language processing. |
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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. |
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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. |
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