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Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model
[article]
2019
arXiv
pre-print
Recently, generating adversarial examples has become an important means of measuring robustness of a deep learning model. Adversarial examples help us identify the susceptibilities of the model and further counter those vulnerabilities by applying adversarial training techniques. In natural language domain, small perturbations in the form of misspellings or paraphrases can drastically change the semantics of the text. We propose a reinforcement learning based approach towards generating
arXiv:1909.07873v1
fatcat:lug35v7xd5d27k5tjy6js6cq5a