Black-box Generation of Adversarial Text Sequences to Evade Deep
Learning Classifiers

Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

13 January 2018

Jack Lanchantin

Papers citing "Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers"

10 / 360 papers shown

Title
Evading classifiers in discrete domains with provable optimality guarantees B. Kulynych Jamie Hayes N. Samarin Carmela Troncoso AAML 21 19 0 25 Oct 2018
Attack Graph Convolutional Networks by Adding Fake Nodes Xiaoyun Wang Minhao Cheng Joe Eaton Cho-Jui Hsieh S. F. Wu AAML GNN 33 78 0 25 Oct 2018
Challenge AI Mind: A Crowd System for Proactive AI Testing Siwei Fu Anbang Xu Xiaotong Liu Huimin Zhou Rama Akkiraju 21 1 0 21 Oct 2018
Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models Tong Niu Joey Tianyi Zhou AAML 21 85 0 06 Sep 2018
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML SILM 14 115 0 31 May 2018
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples Minhao Cheng Jinfeng Yi Pin-Yu Chen Huan Zhang Cho-Jui Hsieh SILM AAML 43 241 0 03 Mar 2018
L2-Nonexpansive Neural Networks Haifeng Qian M. Wegman 25 74 0 22 Feb 2018
Synthetic and Natural Noise Both Break Neural Machine Translation Yonatan Belinkov Yonatan Bisk 61 735 0 06 Nov 2017
Deep Text Classification Can be Fooled Bin Liang Hongcheng Li Miaoqiang Su Pan Bian Xirong Li Wenchang Shi AAML 25 420 0 26 Apr 2017
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Yonghui Wu M. Schuster Z. Chen Quoc V. Le Mohammad Norouzi ... Alex Rudnick Oriol Vinyals G. Corrado Macduff Hughes J. Dean AIMat 716 6,746 0 26 Sep 2016