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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 81–90 of 114 papers

TitleStatusHype
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?—0
Adversarial Text to Continuous Image Generation—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
Adversarial Text Purification: A Large Language Model Approach for Defense—0
Adversarial Text Normalization—0
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods—0
SemAttack: Natural Textual Attacks via Different Semantic Spaces—0
Adversarial Text Generation Without Reinforcement Learning—0
Towards Crafting Text Adversarial Samples—0
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