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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 91–100 of 114 papers

TitleStatusHype
Towards Imperceptible Document Manipulations against Neural Ranking Models—0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
Universal Adversarial Perturbation for Text Classification—0
Graded Suspiciousness of Adversarial Texts to Human—0
Adversarial Text Generation with Dynamic Contextual Perturbation—0
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations—0
Target-driven Attack for Large Language Models—0
Adversarial Text Generation via Sequence Contrast Discrimination—0
Detecting Adversarial Text Attacks via SHapley Additive exPlanations—0
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations—0
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