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Adversarial Text

Adversarial Text refers to a specialised text sequence that is designed specifically to influence the prediction of a language model. Generally, Adversarial Text attack are carried out on Large Language Models (LLMs). Research on understanding different adversarial approaches can help us build effective defense mechanisms to detect malicious text input and build robust language models.

Papers

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Showing 51–75 of 114 papers

TitleStatusHype
Adversarial Training: A simple and efficient technique to Improving NLP Robustness—0
A Grey-box Text Attack Framework using Explainable AI—0
A survey on text generation using generative adversarial networks—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization—0
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation—0
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
Data-Driven Mitigation of Adversarial Text Perturbation—0
Detecting Adversarial Text Attacks via SHapley Additive exPlanations—0
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations—0
DISCO : efficient unsupervised decoding for discrete natural language problems via convex relaxation—0
Don't Search for a Search Method -- Simple Heuristics Suffice for Adversarial Text Attacks—0
Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks—0
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications—0
Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image Prompts with Text Summarization—0
Fooling OCR Systems with Adversarial Text Images—0
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation—0
OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation—0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
Playing to Learn Better: Repeated Games for Adversarial Learning with Multiple Classifiers—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Repairing Adversarial Texts through Perturbation—0
SALSA-TEXT : self attentive latent space based adversarial text generation—0
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments—0
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