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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 31–40 of 114 papers

TitleStatusHype
Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal PerspectiveCode0
NMT-Obfuscator Attack: Ignore a sentence in translation with only one wordCode0
Arabic Synonym BERT-based Adversarial Examples for Text ClassificationCode0
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text ClassificationCode0
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial ExamplesCode0
R.A.C.E.: Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion ModelCode0
Adversarial Text Generation via Feature-Mover's DistanceCode0
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation ProcessCode0
DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example GenerationCode0
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual ModelsCode0
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