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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

Showing 101110 of 114 papers

TitleStatusHype
Universal Adversarial Perturbation for Text Classification0
AdvCodec: Towards A Unified Framework for Adversarial Text Generation0
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial ExamplesCode0
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and EntailmentCode1
TextBugger: Generating Adversarial Text Against Real-world ApplicationsCode0
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text ClassificationCode0
Adversarial Text Generation Without Reinforcement Learning0
SALSA-TEXT : self attentive latent space based adversarial text generation0
Adversarial Text Generation via Feature-Mover's DistanceCode0
Fooling OCR Systems with Adversarial Text Images0
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