TREATED:Towards Universal Defense against Textual Adversarial Attacks

13 September 2021

Papers citing "TREATED:Towards Universal Defense against Textual Adversarial Attacks"

8 / 8 papers shown

Title
Backdoor Learning for NLP: Recent Advances, Challenges, and Future Research Directions Marwan Omar SILM AAML 23 20 0 14 Feb 2023
Don't sweat the small stuff, classify the rest: Sample Shielding to protect text classifiers against adversarial attacks Jonathan Rusert P. Srinivasan AAML 16 3 0 03 May 2022
A Survey of Adversarial Defences and Robustness in NLP Shreyansh Goyal Sumanth Doddapaneni Mitesh M.Khapra B. Ravindran AAML 29 30 0 12 Mar 2022
FreeLB: Enhanced Adversarial Training for Natural Language Understanding Chen Zhu Yu Cheng Zhe Gan S. Sun Tom Goldstein Jingjing Liu AAML 221 436 0 25 Sep 2019
Generating Natural Language Adversarial Examples M. Alzantot Yash Sharma Ahmed Elgohary Bo-Jhang Ho Mani B. Srivastava Kai-Wei Chang AAML 243 914 0 21 Apr 2018
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks Mohit Iyyer John Wieting Kevin Gimpel Luke Zettlemoyer AAML GAN 185 711 0 17 Apr 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 256 3,108 0 04 Nov 2016
Convolutional Neural Networks for Sentence Classification Yoon Kim AILaw VLM 250 13,347 0 25 Aug 2014