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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

Showing 110 of 114 papers

TitleStatusHype
Adversarial Text Generation with Dynamic Contextual Perturbation0
StealthRank: LLM Ranking Manipulation via Stealthy Prompt OptimizationCode0
Breaking BERT: Gradient Attack on Twitter Sentiment Analysis for Targeted MisclassificationCode0
A Grey-box Text Attack Framework using Explainable AI0
Continuous Adversarial Text Representation Learning for Affective Recognition0
SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text GenerationCode0
Hierarchical Lexical Manifold Projection in Large Language Models: A Novel Mechanism for Multi-Scale Semantic Representation0
EMPRA: Embedding Perturbation Rank Attack against Neural Ranking ModelsCode0
Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image Prompts with Text Summarization0
BinarySelect to Improve Accessibility of Black-Box Attack ResearchCode0
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