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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
Adversarial Decoding: Generating Readable Documents for Adversarial ObjectivesCode1
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural PromptsCode1
Breaking BERT: Gradient Attack on Twitter Sentiment Analysis for Targeted MisclassificationCode0
TextBugger: Generating Adversarial Text Against Real-world ApplicationsCode0
BinarySelect to Improve Accessibility of Black-Box Attack ResearchCode0
BERT Lost Patience Won't Be Robust to Adversarial SlowdownCode0
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text AttacksCode0
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation ProcessCode0
StealthRank: LLM Ranking Manipulation via Stealthy Prompt OptimizationCode0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual SimilarityCode0
Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal PerspectiveCode0
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
SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial TextCode0
EMPRA: Embedding Perturbation Rank Attack against Neural Ranking ModelsCode0
Adversarial Text Generation via Feature-Mover's DistanceCode0
NMT-Obfuscator Attack: Ignore a sentence in translation with only one wordCode0
Less is More: Removing Text-regions Improves CLIP Training Efficiency and RobustnessCode0
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
Adversarial Robustness of Neural-Statistical Features in Detection of Generative TransformersCode0
R.A.C.E.: Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion ModelCode0
SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text GenerationCode0
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