AdvCodec: Towards A Unified Framework for Adversarial Text Generation

ICLR 2020 Boxin WangHengzhi PeiHan LiuBo Li

While there has been great interest in generating imperceptible adversarial examples in continuous data domain (e.g. image and audio) to explore the model vulnerabilities, generating \emph{adversarial text} in the discrete domain is still challenging. The main contribution of this paper is to propose a general targeted attack framework AdvCodec for adversarial text generation which addresses the challenge of discrete input space and is easily adapted to general natural language processing (NLP) tasks... (read more)

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