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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 31–40 of 114 papers

TitleStatusHype
A Grey-box Text Attack Framework using Explainable AI—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
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 Representation—0
EMPRA: Embedding Perturbation Rank Attack against Neural Ranking ModelsCode0
Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image Prompts with Text Summarization—0
BinarySelect to Improve Accessibility of Black-Box Attack ResearchCode0
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization—0
TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual SimilarityCode0
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments—0
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