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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 76–100 of 114 papers

TitleStatusHype
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
Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks—0
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations—0
Improving Adversarial Text Generation with n-Gram Matching—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
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
"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation—0
Adversarial Text-to-Image Synthesis: A Review—0
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
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
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
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