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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 1–50 of 114 papers

TitleStatusHype
RETSim: Resilient and Efficient Text SimilarityCode4
Dissecting Adversarial Robustness of Multimodal LM AgentsCode2
RETVec: Resilient and Efficient Text VectorizerCode2
Ignore Previous Prompt: Attack Techniques For Language ModelsCode2
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial ExamplesCode2
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPCode2
BAE: BERT-based Adversarial Examples for Text ClassificationCode2
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion modelsCode1
Adversarial Decoding: Generating Readable Documents for Adversarial ObjectivesCode1
Adversarial Text Rewriting for Text-aware Recommender SystemsCode1
White-box Multimodal Jailbreaks Against Large Vision-Language ModelsCode1
Few-Shot Adversarial Prompt Learning on Vision-Language ModelsCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural PromptsCode1
SemAttack: Natural Textual Attacks via Different Semantic SpacesCode1
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial AttacksCode1
Semantic-Preserving Adversarial Text AttacksCode1
MATE-KD: Masked Adversarial TExt, a Companion to Knowledge DistillationCode1
Persistent Anti-Muslim Bias in Large Language ModelsCode1
Generating Natural Language Attacks in a Hard Label Black Box SettingCode1
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the WildCode1
End-to-End Adversarial Text-to-SpeechCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and EntailmentCode1
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning ClassifiersCode1
Generative Adversarial Text to Image SynthesisCode1
Adversarial Text Generation with Dynamic Contextual Perturbation—0
StealthRank: LLM Ranking Manipulation via Stealthy Prompt OptimizationCode0
Breaking BERT: Gradient Attack on Twitter Sentiment Analysis for Targeted MisclassificationCode0
A Grey-box Text Attack Framework using Explainable AI—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text GenerationCode0
Hierarchical Lexical Manifold Projection in Large Language Models: A Novel Mechanism for Multi-Scale Semantic Representation—0
EMPRA: Embedding Perturbation Rank Attack against Neural Ranking ModelsCode0
Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image Prompts with Text Summarization—0
BinarySelect to Improve Accessibility of Black-Box Attack ResearchCode0
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization—0
TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual SimilarityCode0
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments—0
NMT-Obfuscator Attack: Ignore a sentence in translation with only one wordCode0
IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems—0
Target-driven Attack for Large Language Models—0
Graded Suspiciousness of Adversarial Texts to Human—0
Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine TranslationCode0
OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
Enhancing Adversarial Text Attacks on BERT Models with Projected Gradient Descent—0
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?—0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
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