SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 114 papers

TitleStatusHype
Adversarial Training: A simple and efficient technique to Improving NLP Robustness—0
A Grey-box Text Attack Framework using Explainable AI—0
A survey on text generation using generative adversarial networks—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization—0
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation—0
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
Data-Driven Mitigation of Adversarial Text Perturbation—0
Detecting Adversarial Text Attacks via SHapley Additive exPlanations—0
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations—0
DISCO : efficient unsupervised decoding for discrete natural language problems via convex relaxation—0
Don't Search for a Search Method -- Simple Heuristics Suffice for Adversarial Text Attacks—0
Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks—0
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications—0
Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image Prompts with Text Summarization—0
Fooling OCR Systems with Adversarial Text Images—0
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation—0
OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation—0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
Playing to Learn Better: Repeated Games for Adversarial Learning with Multiple Classifiers—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Repairing Adversarial Texts through Perturbation—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
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods—0
SemAttack: Natural Textual Attacks via Different Semantic Spaces—0
Graded Suspiciousness of Adversarial Texts to Human—0
Target-driven Attack for Large Language Models—0
TextDefense: Adversarial Text Detection based on Word Importance Entropy—0
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks—0
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks—0
"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation—0
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?—0
Towards Crafting Text Adversarial Samples—0
Towards Imperceptible Document Manipulations against Neural Ranking Models—0
Universal Adversarial Perturbation for Text Classification—0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations—0
SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial TextCode0
SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text GenerationCode0
StealthRank: LLM Ranking Manipulation via Stealthy Prompt OptimizationCode0
Less is More: Removing Text-regions Improves CLIP Training Efficiency and RobustnessCode0
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text AttacksCode0
NMT-Obfuscator Attack: Ignore a sentence in translation with only one wordCode0
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of TransformationsCode0
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial ExamplesCode0
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation ProcessCode0
Arabic Synonym BERT-based Adversarial Examples for Text ClassificationCode0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
Show:102550
← PrevPage 2 of 3Next →

No leaderboard results yet.