RMLM: A Flexible Defense Framework for Proactively Mitigating Word-level Adversarial Attacks
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[1] Vikram Pudi,et al. A Strong Baseline for Query Efficient Attacks in a Black Box Setting , 2021, EMNLP.
[2] Cho-Jui Hsieh,et al. Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution , 2021, EMNLP.
[3] Hong Liu,et al. Towards Robustness Against Natural Language Word Substitutions , 2021, ICLR.
[4] Linyang Li,et al. Certified Robustness to Text Adversarial Attacks by Randomized [MASK] , 2021, CL.
[5] Jonathan Berant,et al. Achieving Model Robustness through Discrete Adversarial Training , 2021, EMNLP.
[6] Noseong Park,et al. SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher , 2020, ACL.
[7] Ming Zhou,et al. Neural Deepfake Detection with Factual Structure of Text , 2020, EMNLP.
[8] Shuohang Wang,et al. T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack , 2020, EMNLP.
[9] Zhiyuan Liu,et al. OpenAttack: An Open-source Textual Adversarial Attack Toolkit , 2020, ACL.
[10] Qiang Liu,et al. SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions , 2020, ACL.
[11] Hongtao Wang,et al. Defense of Word-level Adversarial Attacks via Random Substitution Encoding , 2020, KSEM.
[12] Siddhant Garg,et al. BAE: BERT-based Adversarial Examples for Text Classification , 2020, EMNLP.
[13] Xipeng Qiu,et al. BERT-ATTACK: Adversarial Attack against BERT Using BERT , 2020, EMNLP.
[14] Masashi Sugiyama,et al. Do We Need Zero Training Loss After Achieving Zero Training Error? , 2020, ICML.
[15] Florian Tramèr,et al. On Adaptive Attacks to Adversarial Example Defenses , 2020, NeurIPS.
[16] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[17] Sven Gowal,et al. Scalable Verified Training for Provably Robust Image Classification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[18] Kevin Gimpel,et al. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.
[19] T. Goldstein,et al. FreeLB: Enhanced Adversarial Training for Natural Language Understanding , 2019, ICLR.
[20] Kun He,et al. Natural language adversarial defense through synonym encoding , 2019, UAI.
[21] M. Zhou,et al. Reasoning Over Semantic-Level Graph for Fact Checking , 2019, ACL.
[22] Dani Yogatama,et al. Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation , 2019, EMNLP.
[23] Aditi Raghunathan,et al. Certified Robustness to Adversarial Word Substitutions , 2019, EMNLP.
[24] Joey Tianyi Zhou,et al. Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment , 2019, AAAI.
[25] Omer Levy,et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.
[26] Wanxiang Che,et al. Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency , 2019, ACL.
[27] Ming Zhou,et al. Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study , 2019, ACL.
[28] Bhuwan Dhingra,et al. Combating Adversarial Misspellings with Robust Word Recognition , 2019, ACL.
[29] Iryna Gurevych,et al. Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems , 2019, NAACL.
[30] Nina Narodytska,et al. RelGAN: Relational Generative Adversarial Networks for Text Generation , 2018, ICLR.
[31] Carlos Guestrin,et al. Semantically Equivalent Adversarial Rules for Debugging NLP models , 2018, ACL.
[32] Dejing Dou,et al. On Adversarial Examples for Character-Level Neural Machine Translation , 2018, COLING.
[33] Mani B. Srivastava,et al. Generating Natural Language Adversarial Examples , 2018, EMNLP.
[34] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[35] Frank Hutter,et al. Decoupled Weight Decay Regularization , 2017, ICLR.
[36] Alan L. Yuille,et al. Mitigating adversarial effects through randomization , 2017, ICLR.
[37] Sameep Mehta,et al. Towards Crafting Text Adversarial Samples , 2017, ArXiv.
[38] Xirong Li,et al. Deep Text Classification Can be Fooled , 2017, IJCAI.
[39] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[40] Yee Whye Teh,et al. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.
[41] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[42] David Vandyke,et al. Counter-fitting Word Vectors to Linguistic Constraints , 2016, NAACL.
[43] Xiang Zhang,et al. Character-level Convolutional Networks for Text Classification , 2015, NIPS.
[44] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[45] Yoon Kim,et al. Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.
[46] Christopher Potts,et al. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.
[47] Christopher Potts,et al. Learning Word Vectors for Sentiment Analysis , 2011, ACL.
[48] Christiane Fellbaum,et al. Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.
[49] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[50] Xuanjing Huang,et al. Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning , 2022, ACL.
[51] Jiahai Wang,et al. UECA-Prompt: Universal Prompt for Emotion Cause Analysis , 2022, COLING.
[52] Maosong Sun,et al. Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning , 2021, FINDINGS.
[53] Xuanjing Huang,et al. Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble , 2021, ACL.
[54] Danqi Chen,et al. of the Association for Computational Linguistics: , 2001 .