Don't Lag, RAG: Training-Free Adversarial Detection Using RAG
Roie Kazoom, Raz Lapid, Moshe Sipper, Ofer Hadar·April 07, 2025
Summary
VRAG, a training-free framework, leverages VLMs for adversarial attack detection. It retrieves similar patches and images, achieving 95% accuracy with open-source models, setting a new benchmark. It efficiently identifies various adversarial patches with minimal human input, offering robust defense against evolving attacks. A study demonstrated that using simple instructional prompts with contextual examples increased accuracy to 98.00%. Larger few-shot contexts improved model alignment, with UI-TARS-72B-DPO outperforming Qwen-based models and nearing Gemini-2.0's accuracy.
Introduction
Background
Overview of adversarial attacks in machine learning
Importance of robust defense mechanisms
Objective
To introduce VRAG, a novel framework for adversarial attack detection without the need for training
Highlighting the framework's use of VLMs (Visual Language Models) for efficient detection
Method
Data Collection
Description of the dataset used for VRAG's operation
Importance of diverse and representative adversarial examples
Data Preprocessing
Techniques for preparing the data for VRAG's analysis
Explanation of how similar patches and images are retrieved
Implementation
Framework Architecture
Detailed description of VRAG's structure
How it leverages VLMs for adversarial detection
Performance Metrics
Explanation of the metrics used to evaluate VRAG's accuracy
Results showing 95% accuracy with open-source models
Case Study
Simple Instructional Prompts
Description of using simple prompts with contextual examples
Results showing an increase in accuracy to 98.00%
Larger Few-Shot Contexts
Explanation of the impact of larger context on model alignment
Comparison of UI-TARS-72B-DPO with Qwen-based models and Gemini-2.0
Conclusion
VRAG's Contribution
Summary of VRAG's unique features and benefits
Future Directions
Potential areas for further research and development
Practical Applications
Real-world implications and potential uses of VRAG
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does VRAG's performance compare with other models like UI-TARS-72B-DPO and Qwen-based models?
What are the key implementation strategies of VRAG that contribute to its high accuracy in detecting adversarial attacks?
What innovative approaches does VRAG introduce to improve adversarial attack detection accuracy?
How does the VRAG framework utilize VLMs for adversarial attack detection?