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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 26–50 of 114 papers

TitleStatusHype
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?—0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
White-box Multimodal Jailbreaks Against Large Vision-Language ModelsCode1
R.A.C.E.: Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion ModelCode0
Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal PerspectiveCode0
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods—0
Goal-guided Generative Prompt Injection Attack on Large Language Models—0
Few-Shot Adversarial Prompt Learning on Vision-Language ModelsCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual ModelsCode0
Adversarial Text Purification: A Large Language Model Approach for Defense—0
Arabic Synonym BERT-based Adversarial Examples for Text ClassificationCode0
Adversarial Text to Continuous Image Generation—0
RETSim: Resilient and Efficient Text SimilarityCode4
BERT Lost Patience Won't Be Robust to Adversarial SlowdownCode0
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?—0
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of TransformationsCode0
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks—0
Iterative Adversarial Attack on Image-guided Story Ending Generation—0
Less is More: Removing Text-regions Improves CLIP Training Efficiency and RobustnessCode0
Towards Imperceptible Document Manipulations against Neural Ranking Models—0
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation ProcessCode0
Improved Training of Mixture-of-Experts Language GANs—0
RETVec: Resilient and Efficient Text VectorizerCode2
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