ReinforceBug: A Framework to Generate Adversarial Textual Examples (2021.naacl-main)
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| Challenge: | Recent studies have demonstrated that ML Models are sensitive to Adversarial Examples (AEs) AEs are generated by perturbingining examples that preserve the intrinsic utility of the ML solutions but influence the classifier's predictions between original and modified inputs. |
| Approach: | They propose a reinforcement learning framework that learns a policy that is transferable on unseen datasets and generates utility-preserving and transferable AEs. |
| Outcome: | The proposed framework is 10% more successful than the state-of-the-art attack TextFooler. |
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Yaxi Lu, Haolun Li, Xin Cong, Zhong Zhang, Yesai Wu, Yankai Lin, Zhiyuan Liu, Fangming Liu, Maosong Sun
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| Challenge: | Existing work has explored adversarial example generation for natural language understanding tasks, but these examples are often unrealistic and diverge from the real-world data distributions. |
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| Challenge: | Previously studies focused on semantic tasks such as sentiment analysis, question answering and reading comprehension. |
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| Challenge: | Existing methods for generating adversarial samples have deceived many neural inference models, such as text classification and machine translation. |
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