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 1–25 of 114 papers

TitleStatusHype
RETSim: Resilient and Efficient Text SimilarityCode4
Ignore Previous Prompt: Attack Techniques For Language ModelsCode2
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial ExamplesCode2
BAE: BERT-based Adversarial Examples for Text ClassificationCode2
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPCode2
Dissecting Adversarial Robustness of Multimodal LM AgentsCode2
RETVec: Resilient and Efficient Text VectorizerCode2
SemAttack: Natural Textual Attacks via Different Semantic SpacesCode1
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural PromptsCode1
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the WildCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Adversarial Text Rewriting for Text-aware Recommender SystemsCode1
Persistent Anti-Muslim Bias in Large Language ModelsCode1
Semantic-Preserving Adversarial Text AttacksCode1
Generative Adversarial Text to Image SynthesisCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning ClassifiersCode1
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion modelsCode1
Few-Shot Adversarial Prompt Learning on Vision-Language ModelsCode1
Adversarial Decoding: Generating Readable Documents for Adversarial ObjectivesCode1
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and EntailmentCode1
MATE-KD: Masked Adversarial TExt, a Companion to Knowledge DistillationCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
End-to-End Adversarial Text-to-SpeechCode1
Generating Natural Language Attacks in a Hard Label Black Box SettingCode1
Show:102550
← PrevPage 1 of 5Next →

No leaderboard results yet.