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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 51–75 of 114 papers

TitleStatusHype
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
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
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
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation ProcessCode0
Improved Training of Mixture-of-Experts Language GANs—0
TextDefense: Adversarial Text Detection based on Word Importance Entropy—0
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text AttacksCode0
A survey on text generation using generative adversarial networks—0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks—0
Adversarial Text Normalization—0
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations—0
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks—0
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