Adversarial Text
33 papers with code • 0 benchmarks • 2 datasets
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.
Benchmarks
These leaderboards are used to track progress in Adversarial Text
Libraries
Use these libraries to find Adversarial Text models and implementations
Texygen Platform
Most implemented papers
Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers
The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns.
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks
javirandor/wdr
•
•
Adversarial attacks are a major challenge faced by current machine learning research.
SemAttack: Natural Textual Attacks via Different Semantic Spaces
ai-secure/semattack
•
•
Findings (NAACL) 2022
In particular, SemAttack optimizes the generated perturbations constrained on generic semantic spaces, including typo space, knowledge space (e. g., WordNet), contextualized semantic space (e. g., the embedding space of BERT clusterings), or the combination of these spaces.
TAPE: Assessing Few-shot Russian Language Understanding
Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes.
Ignore Previous Prompt: Attack Techniques For Language Models
Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications.
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts
wustl-cspl/riatig
•
•
CVPR 2023
The field of text-to-image generation has made remarkable strides in creating high-fidelity and photorealistic images.
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks
gmum/mango
•
•
We propose a novel gradient-based attack against transformer-based language models that searches for an adversarial example in a continuous space of token probabilities.
RETVec: Resilient and Efficient Text Vectorizer
google-research/retvec
•
•
NeurIPS 2023
The RETVec embedding model is pre-trained using pair-wise metric learning to be robust against typos and character-level adversarial attacks.
Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process
In response, this study proposes a new method called the Fraud's Bargain Attack (FBA), which uses a randomization mechanism to expand the search space and produce high-quality adversarial examples with a higher probability of success.
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion
In this work, we study the problem of adversarial attack generation for Stable Diffusion and ask if an adversarial text prompt can be obtained even in the absence of end-to-end model queries.
