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
Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine TranslationCode0
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of TransformationsCode0
Generating Watermarked Adversarial Texts—0
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation—0
Goal-guided Generative Prompt Injection Attack on Large Language Models—0
Hierarchical Lexical Manifold Projection in Large Language Models: A Novel Mechanism for Multi-Scale Semantic Representation—0
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks—0
IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems—0
Identifying Adversarial Attacks on Text Classifiers—0
Identifying Adversarial Sentences by Analyzing Text Complexity—0
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization—0
Improved Training of Mixture-of-Experts Language GANs—0
Improving Adversarial Text Generation by Modeling the Distant Future—0
Improving Adversarial Text Generation with n-Gram Matching—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
Iterative Adversarial Attack on Image-guided Story Ending Generation—0
AdvCodec: Towards A Unified Framework for Adversarial Text Generation—0
A survey on text generation using generative adversarial networks—0
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation—0
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks—0
OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation—0
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks—0
A Grey-box Text Attack Framework using Explainable AI—0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
Playing to Learn Better: Repeated Games for Adversarial Learning with Multiple Classifiers—0
"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
Show:102550
← PrevPage 2 of 3Next →

No leaderboard results yet.