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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 71–80 of 114 papers

TitleStatusHype
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
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods—0
SemAttack: Natural Textual Attacks via Different Semantic Spaces—0
Graded Suspiciousness of Adversarial Texts to Human—0
Target-driven Attack for Large Language Models—0
TextDefense: Adversarial Text Detection based on Word Importance Entropy—0
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