TextJuggler: Fooling text classification tasks by generating high-quality adversarial examples-Reference-Cited by-同舟云学术

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TextJuggler: Fooling text classification tasks by generating high-quality adversarial examples

Published:2024-09 Issue: Volume:300 Page:112188
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Peng Hao,Wang Zhe^ORCID,Wei Chao,Zhao Dandan,Xu Guangquan,Han Jianming^ORCID,Guo Shixin,Zhong Ming^ORCID,Ji Shouling

Publisher

Elsevier BV

Reference69 articles.

1. Methods for interpreting and understanding deep neural networks;Montavon;Digit. Signal Process.,2018

2. Deep learning for computer vision: A brief review;Voulodimos;Comput. Intell. Neurosci.,2018

3. Ronan Collobert, Jason Weston, A unified architecture for natural language processing: Deep neural networks with multitask learning, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 160–167.

4. Audio-visual speech recognition using deep learning;Noda;Appl. Intell.,2015

5. Intriguing properties of neural networks;Szegedy,2013

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