Papers Adversarial Text
“Adversarial Text” 태그가 달린 논문 114편 · 필터 해제
Adversarial Text Generation with Dynamic Contextual Perturbation
Adversarial attacks on Natural Language Processing (NLP) models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while maintaining human readability. Existing m…
Adversarial TextText GenerationStealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization
The integration of large language models (LLMs) into information retrieval systems introduces new attack surfaces, particularly for adversarial ranking manipulations. We present StealthRank, a novel adversarial ranking a…
Adversarial TextInformation RetrievalProduct RecommendationRecommendation SystemsBreaking BERT: Gradient Attack on Twitter Sentiment Analysis for Targeted Misclassification
Social media platforms like Twitter have increasingly relied on Natural Language Processing NLP techniques to analyze and understand the sentiments expressed in the user generated content. One such state of the art NLP m…
Adversarial TextSentiment AnalysisSentiment ClassificationTwitter Sentiment AnalysisA Grey-box Text Attack Framework using Explainable AI
Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI sys…
Adversarial TextData AugmentationContinuous Adversarial Text Representation Learning for Affective Recognition
While pre-trained language models excel at semantic understanding, they often struggle to capture nuanced affective information critical for affective recognition tasks. To address these limitations, we propose a novel f…
Adversarial TextContrastive LearningEmotion ClassificationRepresentation LearningSMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text Generation
To understand the complexity of sequence classification tasks, Hahn et al. (2021) proposed sensitivity as the number of disjoint subsets of the input sequence that can each be individually changed to change the output. T…
Adversarial TextParaphrase GenerationSensitivitySentence+2Hierarchical Lexical Manifold Projection in Large Language Models: A Novel Mechanism for Multi-Scale Semantic Representation
The integration of structured hierarchical embeddings into transformer-based architectures introduces a refined approach to lexical representation, ensuring that multi-scale semantic relationships are preserved without c…
Adversarial TextComputational EfficiencyEMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models
Recent research has shown that neural information retrieval techniques may be susceptible to adversarial attacks. Adversarial attacks seek to manipulate the ranking of documents, with the intention of exposing users to t…
Adversarial TextInformation RetrievalRe-RankingSentence+1Finding a Wolf in Sheep's Clothing: Combating Adversarial Text-To-Image Prompts with Text Summarization
Text-to-image models are vulnerable to the stepwise "Divide-and-Conquer Attack" (DACA) that utilize a large language model to obfuscate inappropriate content in prompts by wrapping sensitive text in a benign narrative. T…
Adversarial TextBinary ClassificationLanguage ModelingLanguage Modelling+2BinarySelect to Improve Accessibility of Black-Box Attack Research
Adversarial text attack research is useful for testing the robustness of NLP models, however, the rise of transformers has greatly increased the time required to test attacks. Especially when researchers do not have acce…
Adversarial TextPBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization
Understanding the vulnerabilities of Large Vision Language Models (LVLMs) to jailbreak attacks is essential for their responsible real-world deployment. Most previous work requires access to model gradients, or is based …
Adversarial TextPrompt EngineeringTSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual Similarity
Language models based on deep neural networks are vulnerable to textual adversarial attacks. While rich-resource languages like English are receiving focused attention, Tibetan, a cross-border language, is gradually bein…
Adversarial RobustnessAdversarial TextSemantic SimilaritySemantic Textual Similarity+1SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments
Large vision-language models (LVLMs) have shown remarkable capabilities in interpreting visual content. While existing works demonstrate these models' vulnerability to deliberately placed adversarial texts, such texts ar…
Adversarial TextScene UnderstandingNMT-Obfuscator Attack: Ignore a sentence in translation with only one word
Neural Machine Translation systems are used in diverse applications due to their impressive performance. However, recent studies have shown that these systems are vulnerable to carefully crafted small perturbations to th…
Adversarial AttackAdversarial TextMachine TranslationNMT+2IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems
Adversarial examples, which are inputs deliberately perturbed with imperceptible changes to induce model errors, have raised serious concerns for the reliability and security of deep neural networks (DNNs). While adversa…
Adversarial TextSentiment AnalysisTarget-driven Attack for Large Language Models
Current large language models (LLM) provide a strong foundation for large-scale user-oriented natural language tasks. Many users can easily inject adversarial text or instructions through the user interface, thus causing…
Adversarial TextLanguage ModelingLanguage ModellingMisinformationAdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models
Recent advances in diffusion models have significantly enhanced the quality of image synthesis, yet they have also introduced serious safety concerns, particularly the generation of Not Safe for Work (NSFW) content. Prev…
Adversarial TextImage GenerationGraded Suspiciousness of Adversarial Texts to Human
Adversarial examples pose a significant challenge to deep neural networks (DNNs) across both image and text domains, with the intent to degrade model performance through meticulously altered inputs. Adversarial texts, ho…
Adversarial AttackAdversarial TextSemantic SimilaritySemantic Textual Similarity+1Adversarial Decoding: Generating Readable Documents for Adversarial Objectives
We design, implement, and evaluate adversarial decoding, a new, generic text generation technique that produces readable documents for different adversarial objectives. Prior methods either produce easily detectable gibb…
Adversarial TextRAGRetrievalText GenerationVision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation
While neural machine translation (NMT) models achieve success in our daily lives, they show vulnerability to adversarial attacks. Despite being harmful, these attacks also offer benefits for interpreting and enhancing NM…
Adversarial TextMachine TranslationNMTSSIM