Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model

19 March 2022

Papers citing "Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model"

5 / 5 papers shown

Title
DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization Songyang Gao Shihan Dou Yan Liu Xiao Wang Qi Zhang Zhongyu Wei Jin Ma Yingchun Shan OOD 12 3 0 27 Jun 2023
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations Hoang-Quoc Nguyen-Son Seira Hidano Kazuhide Fukushima S. Kiyomoto Isao Echizen 20 0 0 02 Jun 2023
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text Ashim Gupta Carter Blum Temma Choji Yingjie Fei Shalin S Shah Alakananda Vempala Vivek Srikumar AAML 19 8 0 25 May 2023
Certified Robustness to Adversarial Word Substitutions Robin Jia Aditi Raghunathan Kerem Göksel Percy Liang AAML 178 290 0 03 Sep 2019
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks Mohit Iyyer John Wieting Kevin Gimpel Luke Zettlemoyer AAML GAN 185 711 0 17 Apr 2018