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awesome-attack-and-defense-in-nlp
A curated list of attack and defense in NLP. :-)
https://github.com/zhjohnchan/awesome-attack-and-defense-in-nlp
- pull requests
- Attacking Decipherment Problems Optimally with Low-Order N-gram Models
- Attacking Parsing Bottlenecks with Unlabeled Data and Relevant Factorizations
- Supersense Tagging for Arabic: the MT-in-the-Middle Attack
- Identifying attack and support argumentative relations using deep learning
- Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning
- Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts
- Detecting Denial-of-Service Attacks from Social Media Text: Applying NLP to Computer Security
- Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers
- Adversarial Attack on Sentiment Classification
- Adversarial Attack on Sentiment Classification
- Universal Adversarial Triggers for Attacking and Analyzing NLP
- Evaluating adversarial attacks against multiple fact verification systems
- Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack
- Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification
- GEM: Generative Enhanced Model for adversarial attacks
- White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks
- Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
- Weight Poisoning Attacks on Pretrained Models
- Word-level Textual Adversarial Attacking as Combinatorial Optimization
- Detecting Attackable Sentences in Arguments
- Adversarial Attack and Defense of Structured Prediction Models
- Imitation Attacks and Defenses for Black-box Machine Translation Systems
- T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
- BERT-ATTACK: Adversarial Attack Against BERT Using BERT
- Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks
- TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
- Leveraging Extracted Model Adversaries for Improved Black Box Attacks
- Evaluation of Coreference Resolution Systems Under Adversarial Attacks
- Generalization to Mitigate Synonym Substitution Attacks
- Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
- TextAttack: Lessons learned in designing Python frameworks for NLP
- Enhancing Neural Models with Vulnerability via Adversarial Attack
- Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack
- A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples
- From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks
- Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
- Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?
- Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger
- A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger’s Adversarial Attacks
- Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution
- Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble
- Rethinking Stealthiness of Backdoor Attack against NLP Models
- Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models
- An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter
- OpenAttack: An Open-source Textual Adversarial Attack Toolkit
- Concealed Data Poisoning Attacks on NLP Models
- Certified Robustness to Word Substitution Attack with Differential Privacy
- Universal Adversarial Attacks with Natural Triggers for Text Classification
- Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack
- Grey-box Adversarial Attack And Defence For Sentiment Classification
- Contextualized Perturbation for Textual Adversarial Attack
- Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling
- OutFlip: Generating Examples for Unknown Intent Detection with Natural Language Attack
- Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning
- BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks
- Counter-Argument Generation by Attacking Weak Premises
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