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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
R.A.C.E.: Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion ModelCode0
Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal PerspectiveCode0
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods—0
Goal-guided Generative Prompt Injection Attack on Large Language Models—0
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual ModelsCode0
Adversarial Text Purification: A Large Language Model Approach for Defense—0
Arabic Synonym BERT-based Adversarial Examples for Text ClassificationCode0
Adversarial Text to Continuous Image Generation—0
BERT Lost Patience Won't Be Robust to Adversarial SlowdownCode0
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?—0
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of TransformationsCode0
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks—0
Iterative Adversarial Attack on Image-guided Story Ending Generation—0
Less is More: Removing Text-regions Improves CLIP Training Efficiency and RobustnessCode0
Towards Imperceptible Document Manipulations against Neural Ranking Models—0
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation ProcessCode0
Improved Training of Mixture-of-Experts Language GANs—0
TextDefense: Adversarial Text Detection based on Word Importance Entropy—0
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text AttacksCode0
A survey on text generation using generative adversarial networks—0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks—0
Adversarial Text Normalization—0
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations—0
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks—0
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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