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Towards Crafting Text Adversarial Samples

Towards Crafting Text Adversarial Samples

10 July 2017
Suranjana Samanta
S. Mehta
    AAML
ArXivPDFHTML

Papers citing "Towards Crafting Text Adversarial Samples"

29 / 29 papers shown
Title
A Comprehensive Analysis of Adversarial Attacks against Spam Filters
A Comprehensive Analysis of Adversarial Attacks against Spam Filters
Esra Hotoğlu
Sevil Sen
Burcu Can
AAML
29
0
0
04 May 2025
Spiking Convolutional Neural Networks for Text Classification
Spiking Convolutional Neural Networks for Text Classification
Changze Lv
Jianhan Xu
Xiaoqing Zheng
56
27
0
27 Jun 2024
A Survey of Safety and Trustworthiness of Large Language Models through
  the Lens of Verification and Validation
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
Xiaowei Huang
Wenjie Ruan
Wei Huang
Gao Jin
Yizhen Dong
...
Sihao Wu
Peipei Xu
Dengyu Wu
André Freitas
Mustafa A. Mustafa
ALM
45
82
0
19 May 2023
Evaluating the Robustness of Discrete Prompts
Evaluating the Robustness of Discrete Prompts
Yoichi Ishibashi
Danushka Bollegala
Katsuhito Sudoh
Satoshi Nakamura
23
18
0
11 Feb 2023
Why Should Adversarial Perturbations be Imperceptible? Rethink the
  Research Paradigm in Adversarial NLP
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
20
45
0
19 Oct 2022
Dynamic Time Warping based Adversarial Framework for Time-Series Domain
Dynamic Time Warping based Adversarial Framework for Time-Series Domain
Taha Belkhouja
Yan Yan
J. Doppa
AAML
AI4TS
24
25
0
09 Jul 2022
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and
  Theoretical Analysis
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis
Taha Belkhouja
Yan Yan
J. Doppa
OOD
AI4TS
27
9
0
09 Jul 2022
Adversarial Training for Improving Model Robustness? Look at Both
  Prediction and Interpretation
Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation
Hanjie Chen
Yangfeng Ji
OOD
AAML
VLM
26
21
0
23 Mar 2022
Quantifying and Understanding Adversarial Examples in Discrete Input
  Spaces
Quantifying and Understanding Adversarial Examples in Discrete Input Spaces
Volodymyr Kuleshov
Evgenii Nikishin
S. Thakoor
Tingfung Lau
Stefano Ermon
AAML
24
1
0
12 Dec 2021
TREATED:Towards Universal Defense against Textual Adversarial Attacks
TREATED:Towards Universal Defense against Textual Adversarial Attacks
Bin Zhu
Zhaoquan Gu
Le Wang
Zhihong Tian
AAML
36
8
0
13 Sep 2021
Robustness Tests of NLP Machine Learning Models: Search and Semantically
  Replace
Robustness Tests of NLP Machine Learning Models: Search and Semantically Replace
Rahul Singh
Karan Jindal
Yufei Yu
Hanyu Yang
Tarun Joshi
Matthew A. Campbell
Wayne B. Shoumaker
50
2
0
20 Apr 2021
Gradient-based Adversarial Attacks against Text Transformers
Gradient-based Adversarial Attacks against Text Transformers
Chuan Guo
Alexandre Sablayrolles
Hervé Jégou
Douwe Kiela
SILM
106
227
0
15 Apr 2021
Self-Explaining Structures Improve NLP Models
Self-Explaining Structures Improve NLP Models
Zijun Sun
Chun Fan
Qinghong Han
Xiaofei Sun
Yuxian Meng
Fei Wu
Jiwei Li
MILM
XAI
LRM
FAtt
41
38
0
03 Dec 2020
Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood
  Ensemble
Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble
Yi Zhou
Xiaoqing Zheng
Cho-Jui Hsieh
Kai-Wei Chang
Xuanjing Huang
SILM
39
48
0
20 Jun 2020
Differentiable Language Model Adversarial Attacks on Categorical
  Sequence Classifiers
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
I. Fursov
A. Zaytsev
Nikita Klyuchnikov
A. Kravchenko
E. Burnaev
AAML
SILM
29
5
0
19 Jun 2020
Reevaluating Adversarial Examples in Natural Language
Reevaluating Adversarial Examples in Natural Language
John X. Morris
Eli Lifland
Jack Lanchantin
Yangfeng Ji
Yanjun Qi
SILM
AAML
20
111
0
25 Apr 2020
Generating Natural Language Adversarial Examples on a Large Scale with
  Generative Models
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models
Yankun Ren
J. Lin
Siliang Tang
Jun Zhou
Shuang Yang
Yuan Qi
Xiang Ren
GAN
AAML
SILM
29
21
0
10 Mar 2020
Gödel's Sentence Is An Adversarial Example But Unsolvable
Gödel's Sentence Is An Adversarial Example But Unsolvable
Xiaodong Qi
Lansheng Han
AAML
22
0
0
25 Feb 2020
Negative Training for Neural Dialogue Response Generation
Negative Training for Neural Dialogue Response Generation
Tianxing He
James R. Glass
30
59
0
06 Mar 2019
Adversarial Attacks on Deep Learning Models in Natural Language
  Processing: A Survey
Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
W. Zhang
Quan Z. Sheng
A. Alhazmi
Chenliang Li
AAML
24
57
0
21 Jan 2019
TextBugger: Generating Adversarial Text Against Real-world Applications
TextBugger: Generating Adversarial Text Against Real-world Applications
Jinfeng Li
S. Ji
Tianyu Du
Bo Li
Ting Wang
SILM
AAML
46
723
0
13 Dec 2018
Discrete Adversarial Attacks and Submodular Optimization with
  Applications to Text Classification
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
Qi Lei
Lingfei Wu
Pin-Yu Chen
A. Dimakis
Inderjit S. Dhillon
Michael Witbrock
AAML
15
92
0
01 Dec 2018
Attack Graph Convolutional Networks by Adding Fake Nodes
Attack Graph Convolutional Networks by Adding Fake Nodes
Xiaoyun Wang
Minhao Cheng
Joe Eaton
Cho-Jui Hsieh
S. F. Wu
AAML
GNN
30
78
0
25 Oct 2018
Detecting egregious responses in neural sequence-to-sequence models
Detecting egregious responses in neural sequence-to-sequence models
Tianxing He
James R. Glass
AAML
26
22
0
11 Sep 2018
Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue
  Models
Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models
Tong Niu
Joey Tianyi Zhou
AAML
21
85
0
06 Sep 2018
Interpretable Adversarial Perturbation in Input Embedding Space for Text
Interpretable Adversarial Perturbation in Input Embedding Space for Text
Motoki Sato
Jun Suzuki
Hiroyuki Shindo
Yuji Matsumoto
16
188
0
08 May 2018
Adversarial Texts with Gradient Methods
Zhitao Gong
Wenlu Wang
Yangqiu Song
D. Song
Wei-Shinn Ku
AAML
26
77
0
22 Jan 2018
Black-box Generation of Adversarial Text Sequences to Evade Deep
  Learning Classifiers
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Ji Gao
Jack Lanchantin
M. Soffa
Yanjun Qi
AAML
20
706
0
13 Jan 2018
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
287
5,837
0
08 Jul 2016
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