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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 51–100 of 114 papers

TitleStatusHype
TextDefense: Adversarial Text Detection based on Word Importance Entropy—0
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text AttacksCode0
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural PromptsCode1
A survey on text generation using generative adversarial networks—0
Ignore Previous Prompt: Attack Techniques For Language ModelsCode2
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks—0
Adversarial Text Normalization—0
SemAttack: Natural Textual Attacks via Different Semantic SpacesCode1
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks—0
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations—0
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial AttacksCode1
Adversarial Robustness of Neural-Statistical Features in Detection of Generative TransformersCode0
Data-Driven Mitigation of Adversarial Text Perturbation—0
Identifying Adversarial Attacks on Text Classifiers—0
SemAttack: Natural Textual Attacks via Different Semantic Spaces—0
Repairing Adversarial Texts through Perturbation—0
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks—0
Improving Adversarial Text Generation with n-Gram Matching—0
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations—0
Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks—0
Generating Watermarked Adversarial Texts—0
SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial TextCode0
Adversarial Training: A simple and efficient technique to Improving NLP Robustness—0
Don't Search for a Search Method -- Simple Heuristics Suffice for Adversarial Text Attacks—0
Semantic-Preserving Adversarial Text AttacksCode1
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
DISCO : efficient unsupervised decoding for discrete natural language problems via convex relaxation—0
Detecting Adversarial Text Attacks via SHapley Additive exPlanations—0
MATE-KD: Masked Adversarial TExt, a Companion to Knowledge DistillationCode1
"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation—0
Adversarial Text-to-Image Synthesis: A Review—0
Persistent Anti-Muslim Bias in Large Language ModelsCode1
Generating Natural Language Attacks in a Hard Label Black Box SettingCode1
From Unsupervised Machine Translation To Adversarial Text Generation—0
Adversarial Text Generation via Sequence Contrast Discrimination—0
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation—0
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial ExamplesCode2
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the WildCode1
End-to-End Adversarial Text-to-SpeechCode1
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
Improving Adversarial Text Generation by Modeling the Distant Future—0
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPCode2
BAE: BERT-based Adversarial Examples for Text ClassificationCode2
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation—0
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models—0
Playing to Learn Better: Repeated Games for Adversarial Learning with Multiple Classifiers—0
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications—0
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Identifying Adversarial Sentences by Analyzing Text Complexity—0
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