Learning to Attack: Towards Textual Adversarial Attacking in Real-world
Situations

Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations

19 September 2020

Maosong Sun

Papers citing "Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations"

5 / 5 papers shown

Title
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP Yangyi Chen Hongcheng Gao Ganqu Cui Fanchao Qi Longtao Huang Zhiyuan Liu Maosong Sun SILM 14 45 0 19 Oct 2022
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare Muchao Ye Junyu Luo Guanjie Zheng Cao Xiao Ting Wang Fenglong Ma AAML 24 3 0 11 Dec 2021
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer Fanchao Qi Yangyi Chen Xurui Zhang Mukai Li Zhiyuan Liu Maosong Sun AAML SILM 82 175 0 14 Oct 2021
Generating Natural Language Adversarial Examples M. Alzantot Yash Sharma Ahmed Elgohary Bo-Jhang Ho Mani B. Srivastava Kai-Wei Chang AAML 245 914 0 21 Apr 2018
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks Mohit Iyyer John Wieting Kevin Gimpel Luke Zettlemoyer AAML GAN 199 711 0 17 Apr 2018