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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 21–30 of 114 papers

TitleStatusHype
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion modelsCode1
Generative Adversarial Text to Image SynthesisCode1
End-to-End Adversarial Text-to-SpeechCode1
Adversarial Decoding: Generating Readable Documents for Adversarial ObjectivesCode1
Adversarial Text Rewriting for Text-aware Recommender SystemsCode1
SemAttack: Natural Textual Attacks via Different Semantic SpacesCode1
Few-Shot Adversarial Prompt Learning on Vision-Language ModelsCode1
Adversarial Text to Continuous Image Generation—0
Adversarial Text Purification: A Large Language Model Approach for Defense—0
Detecting Adversarial Text Attacks via SHapley Additive exPlanations—0
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