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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 76–100 of 114 papers

TitleStatusHype
"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Repairing Adversarial Texts through Perturbation—0
Adversarial Training: A simple and efficient technique to Improving NLP Robustness—0
Adversarial Text-to-Image Synthesis: A Review—0
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?—0
Adversarial Text to Continuous Image Generation—0
SALSA-TEXT : self attentive latent space based adversarial text generation—0
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments—0
Adversarial Text Purification: A Large Language Model Approach for Defense—0
Adversarial Text Normalization—0
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods—0
SemAttack: Natural Textual Attacks via Different Semantic Spaces—0
Adversarial Text Generation Without Reinforcement Learning—0
Towards Crafting Text Adversarial Samples—0
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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