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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 61–70 of 114 papers

TitleStatusHype
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations—0
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial AttacksCode1
Adversarial Robustness of Neural-Statistical Features in Detection of Generative TransformersCode0
Data-Driven Mitigation of Adversarial Text Perturbation—0
Identifying Adversarial Attacks on Text Classifiers—0
SemAttack: Natural Textual Attacks via Different Semantic Spaces—0
Repairing Adversarial Texts through Perturbation—0
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks—0
Improving Adversarial Text Generation with n-Gram Matching—0
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations—0
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