@inproceedings{tsai-etal-2019-adversarial,
    title = "Adversarial Attack on Sentiment Classification",
    author = "Tsai, Yi-Ting (Alicia)  and
      Yang, Min-Chu  and
      Chen, Han-Yu",
    editor = "Axelrod, Amittai  and
      Yang, Diyi  and
      Cunha, Rossana  and
      Shaikh, Samira  and
      Waseem, Zeerak",
    booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-3653/",
    pages = "166--173",
    abstract = "In this paper, we propose a white-box attack algorithm called {\textquotedblleft}Global Search{\textquotedblright} method and compare it with a simple misspelling noise and a more sophisticated and common white-box attack approach called {\textquotedblleft}Greedy Search{\textquotedblright}. The attack methods are evaluated on the Convolutional Neural Network (CNN) sentiment classifier trained on the IMDB movie review dataset. The attack success rate is used to evaluate the effectiveness of the attack methods and the perplexity of the sentences is used to measure the degree of distortion of the generated adversarial examples. The experiment results show that the proposed {\textquotedblleft}Global Search{\textquotedblright} method generates more powerful adversarial examples with less distortion or less modification to the source text."
}